TestingTrainedNetwork copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install stable-baselines[mpi] tf2onnx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\tf_util.py:58: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\tf_util.py:67: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\policies.py:115: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\a2c\\utils.py:136: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\distributions.py:326: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\distributions.py:327: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:194: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:202: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:210: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:244: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:246: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
      "\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "type object 'FullyConvPolicySmallMap' has no attribute 'observation_space'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_23208\\2921505919.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Define a dummy observation to trace the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m# Create a function to trace the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: type object 'FullyConvPolicySmallMap' has no attribute 'observation_space'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from stable_baselines import PPO2\n",
    "\n",
    "# Load your trained PPO2 model\n",
    "import model\n",
    "model.FullyConvPolicy = model.FullyConvPolicySmallMap\n",
    "# from model import FullyConvPolicySmallMap as FullyConvPolicy\n",
    "\n",
    "ppo2_model = PPO2.load(\"my_models/sokoban/wide/model_1.pkl\")\n",
    "\n",
    "# Get the underlying Keras model from the PPO2 model\n",
    "keras_model = ppo2_model.policy\n",
    "\n",
    "# Define a dummy observation to trace the model\n",
    "obs = keras_model.observation_space.sample().reshape(1, -1)\n",
    "\n",
    "# Create a function to trace the model\n",
    "@tf.function(input_signature=[tf.TensorSpec(shape=obs.shape, dtype=tf.float32)])\n",
    "def serve(obs):\n",
    "    action, _ = keras_model(obs)\n",
    "    return action\n",
    "\n",
    "# Save the Keras model in SavedModel format\n",
    "tf.saved_model.save(keras_model, 'saved_model', signatures={'serving_default': serve})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n"
     ]
    }
   ],
   "source": [
    "#pip install tensorflow==1.15\n",
    "#Install stable-baselines as described in the documentation\n",
    "\n",
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import model\n",
    "model.FullyConvPolicy = model.FullyConvPolicySmallMap\n",
    "# from model import FullyConvPolicySmallMap as FullyConvPolicy\n",
    "\n",
    "model = PPO2.load(\"my_models/sokoban-200//wide/model_1.pkl\")\n",
    "# model.save(\"tests/zelda_turtle_onlymap_3_test\", cloudpickle=False)\n",
    "# model = PPO2.load(\"runs/binary_narrow_no_visits_no_heatmap_no_changes_1_log/latest_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'player': 1, 'crate': 1, 'target': 1, 'regions': 1, 'dist-win': 0, 'sol-length': 13, 'iterations': 10, 'changes': 10, 'max_iterations': 250, 'max_changes': 10, 'terminal_observation': array([[[1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.]],\n",
      "\n",
      "       [[1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [0., 0., 0., 1., 0.],\n",
      "        [1., 0., 0., 0., 0.]],\n",
      "\n",
      "       [[1., 0., 0., 0., 0.],\n",
      "        [0., 0., 1., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.]],\n",
      "\n",
      "       [[0., 0., 0., 0., 1.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.]],\n",
      "\n",
      "       [[1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.]]])}]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def show_state(env, l, c, r, step=0, name=\"\", info=\"\"):\n",
    "    fig = plt.figure(10)\n",
    "    plt.clf()\n",
    "    \n",
    "#     plt.title(\"{} | Step: {} Path: {} Changes: {} Regions: {}\".format(name, step, l[-1], c[-1], r[-1]))\n",
    "    \n",
    "#     ax1 = fig.add_subplot(1,4,1)\n",
    "    ax1 = plt.imshow(env.render(mode='rgb_array'))\n",
    "    plt.axis('off')\n",
    "    \n",
    "#     ax2 = fig.add_subplot(1,4,2)\n",
    "#     ax2 = plt.plot(l)\n",
    "    \n",
    "#     ax3 = fig.add_subplot(1,4,3)\n",
    "#     ax3 = plt.plot(c)\n",
    "    \n",
    "#     ax4 = fig.add_subplot(1,4,4)\n",
    "#     ax4 = plt.plot(r)\n",
    "    \n",
    "    \n",
    "#     fig.set_figwidth(15)\n",
    "#     plt.tight_layout()\n",
    "    \n",
    "    display.clear_output(wait=True)\n",
    "    display.display(plt.gcf())\n",
    "\n",
    "def get_env_fun(p_name, r_name, crop_size, ch_perc):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "env = DummyVecEnv([get_env_fun('sokoban', 'wide', 28, 0.4)])\n",
    "# model = PPO2(CustomPolicy, env, verbose=1, tensorboard_log=\"./runs\")\n",
    "\n",
    "# Enjoy trained agent\n",
    "path_length = []\n",
    "changes = []\n",
    "regions = []\n",
    "obs = env.reset()\n",
    "obs = env.reset()\n",
    "fixed_array = None\n",
    "if fixed_array is not None:\n",
    "    env.get_attr('pcgrl_env')[0]._rep._map = np.array(fixed_array)\n",
    "    env.get_attr('pcgrl_env')[0]._rep._old_map = np.array(fixed_array)\n",
    "frames = [env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']]\n",
    "dones = False\n",
    "i = 0\n",
    "while not dones:\n",
    "    i += 1\n",
    "    action, _states = model.predict(obs)\n",
    "    #action = np.array([env.action_space.sample()])\n",
    "    frames.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "    obs, rewards, dones, info = env.step(action)\n",
    "#     path_length.append(info[0]['path-length'])\n",
    "#     changes.append(rewards[0])\n",
    "#     regions.append(info[0]['regions'])\n",
    "    if dones:\n",
    "#         print(\"YAAAAAAAAAAAAAY\")\n",
    "        break\n",
    "    show_state(env, path_length, changes, regions, i)\n",
    "plt.show()\n",
    "print(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Sokoban Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: netron in c:\\python37\\lib\\site-packages (7.2.1)Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.2.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install netron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!netron F:\\learning\\master\\PCGRL\\gym-pcgrl\\my_models\\binary\\narrow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnpicklingError",
     "evalue": "A load persistent id instruction was encountered,\nbut no persistent_load function was specified.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnpicklingError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11912\\706951402.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Load the model from the pickle file using dill\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'my_models/sokoban-200/wide/model_1.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdill\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# Now you can proceed with the conversion to ONNX and visualization steps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\dill\\_dill.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, ignore, **kwds)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[0mSee\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0marguments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m     \"\"\"\n\u001b[1;32m--> 287\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\dill\\_dill.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#NOTE: if settings change, need to update attributes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 442\u001b[1;33m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStockUnpickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    443\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_main_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__name__'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    444\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnpicklingError\u001b[0m: A load persistent id instruction was encountered,\nbut no persistent_load function was specified."
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import tensorflow as tf\n",
    "\n",
    "import dill\n",
    "\n",
    "# Load the model from the pickle file using dill\n",
    "with open('my_models/sokoban-200/wide/model_1.pkl', 'rb') as file:\n",
    "    model = dill.load(file)\n",
    "\n",
    "# Now you can proceed with the conversion to ONNX and visualization steps\n",
    "\n",
    "import tf2onnx\n",
    "\n",
    "# Convert the model to ONNX format\n",
    "onnx_model = tf2onnx.convert.from_sklearn(model)\n",
    "\n",
    "# Save the ONNX model to a file\n",
    "with open('model.onnx', 'wb') as file:\n",
    "    file.write(onnx_model.SerializeToString())\n",
    "\n",
    "# Convert the loaded model to H5 format\n",
    "model.save('model.h5')\n",
    "\n",
    "print(\"Model has been saved in H5 format.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model model_1\n",
      "my_models/sokoban/narrow/model_1.pkl\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing model_1_1 at change percentage of 0.0\n",
      "Testing model_1_1 at change percentage of 0.1\n",
      "Testing model_1_1 at change percentage of 0.2\n",
      "Testing model_1_1 at change percentage of 0.30000000000000004\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3412\\3678178984.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    245\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Testing {}_{} at change percentage of {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm_index\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[0menv_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcrop_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m             \u001b[0mtemp_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_result\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3412\\3678178984.py\u001b[0m in \u001b[0;36msample_data\u001b[1;34m(sample_size, env_func, model, lambdas)\u001b[0m\n\u001b[0;32m    202\u001b[0m             \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m             \u001b[0mlast_lvl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pcgrl_env'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_observation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'map'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 204\u001b[1;33m             \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    205\u001b[0m         \u001b[0mlvls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlast_lvl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\vec_env\\base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, actions)\u001b[0m\n\u001b[0;32m    132\u001b[0m         \"\"\"\n\u001b[0;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\vec_env\\dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     39\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuf_rews\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuf_infos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menvs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m                 \u001b[1;31m# save final observation where user can get it, then reset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\gym\\core.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\wrappers.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_action\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\wrappers.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_action\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\wrappers.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m         \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_action\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m         \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\pcgrl_env.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    136\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_changes\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mchange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_heatmap\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rep_stats\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_stats\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_string_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tile_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m         \u001b[1;31m# calculate the values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[0mobservation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_observation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban_prob.py\u001b[0m in \u001b[0;36mget_stats\u001b[1;34m(self, map)\u001b[0m\n\u001b[0;32m    142\u001b[0m         }\n\u001b[0;32m    143\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"player\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"crate\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"target\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"crate\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"regions\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 144\u001b[1;33m                 \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"dist-win\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"solution\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_game\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    145\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmap_stats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban_prob.py\u001b[0m in \u001b[0;36m_run_game\u001b[1;34m(self, map)\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msolState\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckWin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msol\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m         \u001b[0msol\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msolState\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maStarAgent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSolution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_solver_power\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msolState\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckWin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msol\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban\\engine.py\u001b[0m in \u001b[0;36mgetSolution\u001b[1;34m(self, state, balance, maxIterations)\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0miterations\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m             \u001b[1;31m# queue = sorted(queue, key=lambda node: balance*node.getCost() + node.getHeuristic())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m             \u001b[0mcurrent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqueue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    108\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcurrent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckWin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mcurrent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetActions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\queue.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m    178\u001b[0m                         \u001b[1;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m             \u001b[0mitem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\queue.py\u001b[0m in \u001b[0;36m_get\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 236\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mheappop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    237\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban\\engine.py\u001b[0m in \u001b[0;36m__lt__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__lt__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHeuristic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mNode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetCost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHeuristic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mNode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetCost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mAgent\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban\\engine.py\u001b[0m in \u001b[0;36mgetHeuristic\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetHeuristic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHeuristic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheckWin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\learning\\master\\PCGRL\\gym-pcgrl\\gym_pcgrl\\envs\\probs\\sokoban\\engine.py\u001b[0m in \u001b[0;36mgetHeuristic\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    289\u001b[0m             \u001b[0mbestMatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 291\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[0mbestDist\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    292\u001b[0m                     \u001b[0mbestMatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    293\u001b[0m                     \u001b[0mbestDist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binary_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "p_name = \"sokoban\"\n",
    "crop_size = 10\n",
    "sample_size = 100\n",
    "rep_names = [\"narrow\",\"turtle\",\"wide\"]\n",
    "exp_name = '1'\n",
    "number_of_models = 1\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "result = {}\n",
    "for i in range(len(rep_names)):\n",
    "    r_name = rep_names[i]\n",
    "    if isinstance(exp_name,list):\n",
    "        e_name = exp_name[i]\n",
    "    else:\n",
    "        e_name = exp_name\n",
    "    for m_index in range(number_of_models):\n",
    "       # m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "        m_name = \"model_1\"\n",
    "        print(\"Loading model {}\".format(m_name))\n",
    "        add_index = \"\"\n",
    "        if number_of_models > 1:\n",
    "            add_index = \"_\" + str(m_index + 1)\n",
    "        e_format = 'pkl'\n",
    "        if not os.path.exists('my_models/sokoban/{}{}.{}'.format(m_name, add_index, e_format)):\n",
    "            e_format = 'pkl'\n",
    "        dir = 'my_models/sokoban/{}/{}{}.{}'.format(r_name,m_name, add_index, e_format)\n",
    "        print(dir)\n",
    "        model = PPO2.load(dir)\n",
    "        if m_name not in result:\n",
    "            result[m_name] = {}\n",
    "        for ch_index in range(len(ch_perc_array)):\n",
    "            ch_perc = ch_perc_array[ch_index]\n",
    "            print(\"Testing {}_{} at change percentage of {}\".format(m_name, m_index+1, ch_perc))\n",
    "            env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "            temp_result = sample_data(sample_size, env_func, model, lambdas[p_name])\n",
    "            for name in temp_result:\n",
    "                if not(name in result[m_name]):\n",
    "                    result[m_name][name] = []\n",
    "                if number_of_models > 1:\n",
    "                    if m_index == 0:\n",
    "                        result[m_name][name].append([np.mean(temp_result[name])])\n",
    "                    else:\n",
    "                        result[m_name][name][ch_index].append(np.mean(temp_result[name]))\n",
    "                else:\n",
    "                    result[m_name][name].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overcoming the serialization issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "# from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'has_sol': lambda info: [0, 1][info['path-length'] >= 1 and info['nearest-enemy'] >= 4],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binaryimp_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            if model is None:\n",
    "                action = [env.action_space.sample()]\n",
    "            else:\n",
    "                action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "def get_result_dict(ch_perc_array, lambdafuncs):\n",
    "    result = {}\n",
    "    for name in lambdafuncs:\n",
    "        result[name] = []\n",
    "        for _ in ch_perc_array:\n",
    "            result[name].append([])\n",
    "    result['hdiversity'] = []        \n",
    "    for _ in ch_perc_array:\n",
    "        result['hdiversity'].append([])\n",
    "    return result\n",
    "\n",
    "def get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result):\n",
    "    for ch_index in range(len(ch_perc_array)):\n",
    "        ch_perc = ch_perc_array[ch_index]\n",
    "        print(\"Testing {}_{} at change percentage of {}\".format(p_name, r_name, ch_perc))\n",
    "        env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "        temp_result = sample_data(sample_size, env_func, model, lambdafuncs)\n",
    "        for name in temp_result:\n",
    "            result[name][ch_index].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_name = \"sokoban\"\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "lambdafuncs = lambdas[p_name]\n",
    "\n",
    "result = {}\n",
    "result['{}_narrow'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_wide'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24], [1.2, 1.05, 1.05, 0.88, 0.88], [0.8, 0.725, 0.725, 0.82, 0.8], [0.675, 0.675, 0.7, 0.69, 0.64], [0.675, 0.525, 0.475, 0.66, 0.47], [0.475, 0.625, 0.625, 0.63, 0.54], [0.55, 0.475, 0.525, 0.55, 0.44], [0.525, 0.5, 0.475, 0.44, 0.64], [0.525, 0.5, 0.575, 0.47, 0.41], [0.425, 0.4, 0.4, 0.43, 0.48], [0.425, 0.6, 0.45, 0.32, 0.38]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38], [1.25, 0.875, 1.35, 1.33, 1.18], [0.95, 1.15, 1.125, 1.17, 1.2], [0.9, 1.2, 1.2, 1.18, 1.02], [0.825, 0.8, 0.925, 0.77, 0.7], [0.675, 0.65, 0.525, 0.67, 0.86], [0.3, 0.475, 0.55, 0.61, 0.57], [0.4, 0.425, 0.725, 0.42, 0.44], [0.275, 0.475, 0.275, 0.37, 0.43], [0.375, 0.225, 0.35, 0.22, 0.42], [0.375, 0.475, 0.375, 0.34, 0.33]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42], [1.625, 1.35, 1.5, 1.35, 1.38], [1.175, 1.2, 1.275, 1.23, 1.18], [1.05, 1.125, 1.225, 1.17, 0.99], [0.875, 0.925, 0.85, 0.93, 0.84], [0.8, 0.725, 0.675, 0.85, 0.96], [0.4, 0.55, 0.7, 0.6, 0.55], [0.5, 0.45, 0.775, 0.4, 0.5], [0.275, 0.45, 0.325, 0.32, 0.56], [0.4, 0.225, 0.425, 0.25, 0.39], [0.35, 0.425, 0.375, 0.33, 0.29]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77], [1.425, 1.15, 1.425, 1.5, 1.33], [1.0, 1.0, 1.025, 1.03, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39, 0.11], [0.925, 0.45, 0.25, 1.22, 1.38], [1.725, 0.475, 1.075, 0.63, 0.24], [0.775, 0.4, 1.275, 1.61, 0.46], [2.525, 0.575, 2.275, 0.87, 1.45], [0.85, 1.45, 0.4, 0.69, 0.9], [0.725, 1.425, 2.375, 0.93, 1.17], [1.275, 1.675, 0.875, 0.77, 1.47], [0.625, 0.0, 0.675, 1.24, 0.79], [0.525, 2.45, 0.0, 0.08, 0.72]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0], [2.175, 2.05, 2.375, 2.85, 2.57], [6.475, 8.1, 13.525, 9.3, 8.19], [19.725, 13.6, 18.35, 28.22, 21.16], [54.25, 47.2, 38.275, 54.5, 62.0], [110.6, 98.95, 81.8, 95.56, 79.77], [214.15, 168.825, 167.775, 184.01, 183.16], [202.95, 230.725, 258.525, 274.84, 277.62], [414.7, 351.45, 396.05, 366.86, 370.9], [442.8, 469.35, 432.2, 478.19, 429.28], [549.075, 479.95, 541.35, 542.02, 529.55]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97], [9.85, 9.875, 9.95, 9.7, 9.85], [11.225, 11.775, 11.375, 11.47, 11.5], [13.95, 13.75, 14.575, 13.97, 13.87], [15.65, 14.925, 14.625, 15.15, 15.35], [15.55, 16.575, 16.6, 17.1, 16.8], [17.225, 16.875, 18.025, 16.82, 17.26], [18.2, 17.8, 18.45, 19.62, 18.84]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38], [0.375, 0.575, 0.45, 0.43, 0.47], [0.7, 0.675, 0.675, 0.74, 0.68], [0.675, 0.575, 0.65, 0.67, 0.6], [0.575, 0.525, 0.475, 0.66, 0.47], [0.475, 0.625, 0.625, 0.61, 0.54], [0.5, 0.475, 0.525, 0.55, 0.44], [0.475, 0.5, 0.475, 0.44, 0.62], [0.525, 0.5, 0.575, 0.47, 0.41], [0.425, 0.4, 0.4, 0.43, 0.48], [0.425, 0.6, 0.45, 0.32, 0.38]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22], [0.2, 0.125, 0.3, 0.31, 0.26], [0.45, 0.4, 0.475, 0.41, 0.41], [0.525, 0.375, 0.475, 0.52, 0.37], [0.475, 0.375, 0.45, 0.43, 0.33], [0.3, 0.3, 0.35, 0.27, 0.38], [0.2, 0.3, 0.225, 0.24, 0.25], [0.3, 0.3, 0.4, 0.14, 0.22], [0.225, 0.225, 0.225, 0.19, 0.23], [0.225, 0.125, 0.275, 0.12, 0.19], [0.275, 0.25, 0.225, 0.19, 0.16]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03, 0.03], [0.125, 0.025, 0.05, 0.11, 0.1], [0.125, 0.075, 0.075, 0.08, 0.04], [0.075, 0.05, 0.075, 0.14, 0.06], [0.15, 0.125, 0.175, 0.08, 0.11], [0.125, 0.075, 0.075, 0.05, 0.07], [0.05, 0.125, 0.15, 0.06, 0.11], [0.075, 0.075, 0.125, 0.05, 0.1], [0.075, 0.0, 0.075, 0.06, 0.05], [0.1, 0.15, 0.0, 0.02, 0.06]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04], [0.05, 0.0, 0.05, 0.0, 0.0], [0.0, 0.0, 0.025, 0.04, 0.01], [0.075, 0.0, 0.075, 0.02, 0.04], [0.025, 0.075, 0.0, 0.02, 0.04], [0.025, 0.05, 0.075, 0.03, 0.02], [0.05, 0.075, 0.0, 0.03, 0.06], [0.025, 0.0, 0.025, 0.04, 0.03], [0.0, 0.1, 0.0, 0.0, 0.02]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('{}_results.json'.format(p_name)) as fp:\n",
    "    result = json.load(fp)\n",
    "    \n",
    "print(result)\n",
    "result['{}_random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_wide random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing sokoban_narrow\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing sokoban_narrow at change percentage of 0.0\n",
      "Testing sokoban_narrow at change percentage of 0.1\n",
      "Testing sokoban_narrow at change percentage of 0.2\n",
      "Testing sokoban_narrow at change percentage of 0.30000000000000004\n",
      "Testing sokoban_narrow at change percentage of 0.4\n",
      "Testing sokoban_narrow at change percentage of 0.5\n",
      "Testing sokoban_narrow at change percentage of 0.6000000000000001\n",
      "Testing sokoban_narrow at change percentage of 0.7000000000000001\n",
      "Testing sokoban_narrow at change percentage of 0.8\n",
      "Testing sokoban_narrow at change percentage of 0.9\n",
      "Testing sokoban_narrow at change percentage of 1.0\n",
      "Testing sokoban_turtle\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing sokoban_turtle at change percentage of 0.0\n",
      "Testing sokoban_turtle at change percentage of 0.1\n",
      "Testing sokoban_turtle at change percentage of 0.2\n",
      "Testing sokoban_turtle at change percentage of 0.30000000000000004\n",
      "Testing sokoban_turtle at change percentage of 0.4\n",
      "Testing sokoban_turtle at change percentage of 0.5\n",
      "Testing sokoban_turtle at change percentage of 0.6000000000000001\n",
      "Testing sokoban_turtle at change percentage of 0.7000000000000001\n",
      "Testing sokoban_turtle at change percentage of 0.8\n",
      "Testing sokoban_turtle at change percentage of 0.9\n",
      "Testing sokoban_turtle at change percentage of 1.0\n",
      "Testing sokoban_wide\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing sokoban_wide at change percentage of 0.0\n",
      "Testing sokoban_wide at change percentage of 0.1\n",
      "Testing sokoban_wide at change percentage of 0.2\n",
      "Testing sokoban_wide at change percentage of 0.30000000000000004\n",
      "Testing sokoban_wide at change percentage of 0.4\n",
      "Testing sokoban_wide at change percentage of 0.5\n",
      "Testing sokoban_wide at change percentage of 0.6000000000000001\n",
      "Testing sokoban_wide at change percentage of 0.7000000000000001\n",
      "Testing sokoban_wide at change percentage of 0.8\n",
      "Testing sokoban_wide at change percentage of 0.9\n",
      "Testing sokoban_wide at change percentage of 1.0\n"
     ]
    }
   ],
   "source": [
    "crop_size = 10\n",
    "sample_size = 100\n",
    "r_names = [\"narrow\",\"turtle\",\"wide\"]\n",
    "for i in range(len(r_names)):\n",
    "    r_name = r_names[i]\n",
    "    print(\"Testing {}_{}\".format(p_name, r_name))\n",
    "    model = None\n",
    "    model = PPO2.load('my_models/{}/{}/model_1.pkl'.format(p_name, r_name))\n",
    "    res_name = r_name\n",
    "    if model is None:\n",
    "        res_name = \"random\"\n",
    "    get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result['{}_{}'.format(p_name, res_name)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sokoban\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print(p_name)\n",
    "with open('{}_results.json'.format(p_name), 'w') as fp:\n",
    "    json.dump(result, fp)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Model Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for name in p_dict:\n",
    "        plt.plot(np.array(ch_perc_array),p_dict[name])\n",
    "        names.append(name)\n",
    "    plt.legend(names)\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\")\n",
    "    for n in lambdas[p_name]:\n",
    "        plt_dict(get_data(result, n), n, '{}_{}_{}'.format(p_name, exp_name, n))\n",
    "    plt_dict(get_data(result, 'hdiversity'), 'hamming diversity', '{}_{}_{}'.format(p_name,exp_name,'hammingdiversity'))\n",
    "    plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "    plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "    plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Graphs with multi models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player\n",
      "sokoban_narrow {'sokoban_narrow': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} crate\n",
      "sokoban_narrow {'sokoban_narrow': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'sokoban_turtle': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'sokoban_turtle': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'sokoban_turtle': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} target\n",
      "sokoban_narrow {'sokoban_narrow': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'sokoban_turtle': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'sokoban_turtle': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'sokoban_turtle': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} regions\n",
      "sokoban_narrow {'sokoban_narrow': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} sollength\n",
      "sokoban_narrow {'sokoban_narrow': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'sokoban_turtle': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'sokoban_turtle': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'sokoban_turtle': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 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0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 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1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} iterations\n",
      "sokoban_narrow {'sokoban_narrow': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'sokoban_turtle': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'sokoban_turtle': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'sokoban_turtle': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} changes\n",
      "sokoban_narrow {'sokoban_narrow': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'sokoban_turtle': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'sokoban_turtle': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'sokoban_turtle': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 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0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 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0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player_const\n",
      "sokoban_narrow {'sokoban_narrow': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'sokoban_turtle': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} ratio_const\n",
      "sokoban_narrow {'sokoban_narrow': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'sokoban_turtle': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'sokoban_turtle': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'sokoban_turtle': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 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1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} success\n",
      "sokoban_narrow {'sokoban_narrow': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'sokoban_turtle': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'sokoban_turtle': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'sokoban_turtle': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41, 1.22], [1.2, 1.31, 1.19], [1.15, 1.03, 1.03], [1.05, 0.98, 1.03], [0.975, 0.97, 0.97], [1.0, 0.97, 0.98], [1.0, 0.98, 1.0], [1.0, 0.98, 0.95], [0.95, 1.01, 0.99], [1.0, 0.97, 0.99], [1.0, 0.98, 1.0]], 'crate': [[1.5, 1.27, 1.32], [1.3, 1.29, 1.36], [1.425, 1.3, 1.36], [1.5, 1.34, 1.42], [1.325, 1.44, 1.37], [1.325, 1.55, 1.44], [1.5, 1.51, 1.47], [1.5, 1.44, 1.42], [1.425, 1.41, 1.41], [1.425, 1.43, 1.43], [1.325, 1.39, 1.56]], 'target': [[1.425, 1.36, 1.44], [1.375, 1.37, 1.49], [1.425, 1.43, 1.42], [1.475, 1.36, 1.45], [1.325, 1.44, 1.39], [1.275, 1.56, 1.43], [1.5, 1.51, 1.47], [1.5, 1.46, 1.42], [1.425, 1.42, 1.41], [1.425, 1.43, 1.42], [1.325, 1.4, 1.56]], 'regions': [[2.175, 2.15, 2.13], [1.65, 1.73, 1.84], [1.1, 1.03, 1.05], [1.025, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.275, 0.08, 0.14], [1.075, 0.34, 0.53], [3.55, 5.31, 4.59], [6.15, 7.72, 7.15], [7.35, 9.16, 9.92], [11.075, 8.84, 8.47], [9.825, 8.36, 9.61], [9.525, 8.53, 8.77], [8.75, 9.18, 9.08], [9.05, 9.54, 9.7], [8.7, 9.42, 9.4]], 'iterations': [[3.55, 4.34, 4.65], [8.25, 8.17, 8.26], [34.075, 32.51, 39.64], [74.025, 74.14, 62.46], [186.025, 186.45, 168.81], [201.55, 259.44, 248.01], [322.85, 340.22, 331.55], [381.575, 387.66, 375.07], [441.475, 454.28, 443.8], [497.475, 492.95, 477.02], [582.675, 540.09, 547.75]], 'changes': [[1.0, 1.0, 0.99], [2.0, 2.0, 2.0], [4.7, 4.72, 4.74], [6.225, 6.13, 6.35], [7.925, 7.32, 7.67], [7.825, 7.84, 7.76], [7.825, 8.07, 7.76], [8.0, 7.76, 7.76], [7.8, 8.16, 8.03], [7.775, 8.29, 7.96], [7.575, 7.72, 7.9]], 'player_const': [[0.425, 0.5, 0.5], [0.825, 0.73, 0.71], [0.775, 0.97, 0.89], [0.9, 0.98, 0.94], [0.975, 0.97, 0.97], [1.0, 0.95, 0.98], [1.0, 0.98, 0.98], [1.0, 0.98, 0.95], [0.95, 0.99, 0.99], [0.9, 0.97, 0.99], [1.0, 0.98, 1.0]], 'ratio_const': [[0.275, 0.3, 0.37], [0.6, 0.43, 0.44], [0.8, 0.82, 0.82], [0.975, 0.96, 0.89], [1.0, 0.98, 0.98], [0.975, 0.99, 0.99], [1.0, 0.98, 1.0], [1.0, 0.98, 1.0], [1.0, 0.99, 0.99], [1.0, 1.0, 0.99], [1.0, 0.99, 1.0]], 'has_sol': [[0.025, 0.01, 0.01], [0.1, 0.04, 0.04], [0.35, 0.55, 0.47], [0.65, 0.72, 0.66], [0.875, 0.85, 0.92], [0.95, 0.87, 0.82], [0.925, 0.84, 0.91], [0.875, 0.87, 0.87], [0.85, 0.93, 0.93], [0.9, 0.9, 0.9], [0.925, 0.92, 0.9]], 'success': [[0.0, 0.0, 0.0], [0.025, 0.0, 0.01], [0.025, 0.07, 0.03], [0.1, 0.15, 0.11], [0.05, 0.11, 0.14], [0.225, 0.07, 0.09], [0.15, 0.1, 0.13], [0.125, 0.09, 0.14], [0.125, 0.1, 0.12], [0.1, 0.11, 0.14], [0.075, 0.15, 0.13]], 'hdiversity': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]]}, 'sokoban_turtle': {'player': [[1.275, 1.26, 1.21], [1.125, 1.17, 1.19], [1.05, 1.01, 1.02], [1.025, 1.0, 1.05], [1.05, 0.99, 1.01], [0.975, 1.03, 1.0], [1.0, 1.0, 1.0], [0.975, 0.99, 0.98], [1.0, 1.01, 1.03], [1.025, 1.02, 1.01], [1.0, 1.02, 1.03]], 'crate': [[1.275, 1.37, 1.21], [1.1, 1.39, 1.33], [1.425, 1.4, 1.39], [1.525, 1.5, 1.52], [1.375, 1.5, 1.4], [1.575, 1.45, 1.29], [1.25, 1.42, 1.43], [1.625, 1.48, 1.41], [1.375, 1.49, 1.43], [1.45, 1.54, 1.58], [1.45, 1.43, 1.49]], 'target': [[1.4, 1.36, 1.28], [1.35, 1.44, 1.27], [1.475, 1.38, 1.42], [1.45, 1.5, 1.51], [1.35, 1.55, 1.38], [1.5, 1.42, 1.3], [1.25, 1.42, 1.42], [1.625, 1.43, 1.42], [1.375, 1.48, 1.42], [1.425, 1.5, 1.55], [1.425, 1.38, 1.47]], 'regions': [[2.1, 2.27, 1.94], [1.425, 1.65, 1.55], [1.025, 1.03, 1.03], [1.0, 1.0, 1.01], [1.05, 1.01, 1.03], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.02, 1.04], [1.05, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'sollength': [[0.125, 0.07, 0.26], [0.975, 0.86, 0.5], [5.625, 6.29, 5.42], [7.775, 8.24, 6.97], [8.975, 9.04, 9.66], [8.65, 9.77, 9.03], [9.5, 9.85, 9.68], [10.825, 9.18, 9.18], [9.875, 9.89, 10.04], [11.35, 10.19, 9.34], [9.325, 9.95, 10.19]], 'iterations': [[3.275, 3.19, 3.45], [9.25, 7.88, 8.48], [45.5, 55.93, 50.19], [117.125, 123.67, 130.24], [193.025, 217.47, 204.76], [264.125, 257.52, 268.5], [339.825, 329.88, 320.78], [369.25, 392.35, 371.7], [419.4, 443.11, 431.63], [435.45, 488.21, 465.08], [569.1, 539.77, 522.31]], 'changes': [[1.0, 1.0, 0.99], [1.975, 1.93, 1.96], [4.525, 4.44, 4.71], [5.725, 5.07, 5.39], [6.725, 6.05, 6.05], [6.1, 6.64, 6.6], [6.3, 6.23, 6.35], [6.375, 6.22, 6.59], [7.175, 6.76, 6.22], [6.5, 6.81, 6.73], [6.75, 6.64, 6.43]], 'player_const': [[0.6, 0.69, 0.64], [0.85, 0.76, 0.81], [0.95, 0.97, 0.94], [0.975, 0.96, 0.94], [0.95, 0.97, 0.99], [0.975, 0.97, 0.98], [1.0, 1.0, 0.98], [0.975, 0.96, 0.96], [0.95, 0.97, 0.97], [0.975, 0.96, 0.97], [1.0, 0.98, 0.97]], 'ratio_const': [[0.35, 0.36, 0.3], [0.475, 0.42, 0.44], [0.825, 0.83, 0.76], [0.95, 0.91, 0.91], [0.975, 0.97, 0.94], [0.925, 0.96, 0.99], [1.0, 0.97, 0.99], [0.95, 0.96, 0.97], [0.925, 0.99, 0.99], [0.975, 0.96, 0.97], [0.975, 0.95, 0.96]], 'has_sol': [[0.025, 0.01, 0.04], [0.125, 0.08, 0.06], [0.475, 0.59, 0.49], [0.725, 0.77, 0.72], [0.8, 0.82, 0.83], [0.775, 0.84, 0.89], [1.0, 0.89, 0.92], [0.875, 0.83, 0.83], [0.825, 0.87, 0.92], [0.85, 0.84, 0.85], [0.9, 0.9, 0.83]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.02, 0.0], [0.075, 0.08, 0.05], [0.075, 0.11, 0.05], [0.125, 0.12, 0.17], [0.175, 0.16, 0.11], [0.1, 0.14, 0.17], [0.175, 0.1, 0.14], [0.15, 0.12, 0.15], [0.225, 0.14, 0.17], [0.1, 0.16, 0.18]], 'hdiversity': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29, 1.24, 1.15], [1.2, 1.05, 1.05, 0.88, 0.88, 1.32], [0.8, 0.725, 0.725, 0.82, 0.8, 1.02], [0.675, 0.675, 0.7, 0.69, 0.64, 0.98], [0.675, 0.525, 0.475, 0.66, 0.47, 0.97], [0.475, 0.625, 0.625, 0.63, 0.54, 0.91], [0.55, 0.475, 0.525, 0.55, 0.44, 0.94], [0.525, 0.5, 0.475, 0.44, 0.64, 0.98], [0.525, 0.5, 0.575, 0.47, 0.41, 0.92], [0.425, 0.4, 0.4, 0.43, 0.48, 0.99], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'crate': [[1.175, 1.05, 1.1, 1.21, 1.38, 1.57], [1.25, 0.875, 1.35, 1.33, 1.18, 1.47], [0.95, 1.15, 1.125, 1.17, 1.2, 1.48], [0.9, 1.2, 1.2, 1.18, 1.02, 1.45], [0.825, 0.8, 0.925, 0.77, 0.7, 1.36], [0.675, 0.65, 0.525, 0.67, 0.86, 1.4], [0.3, 0.475, 0.55, 0.61, 0.57, 1.17], [0.4, 0.425, 0.725, 0.42, 0.44, 1.14], [0.275, 0.475, 0.275, 0.37, 0.43, 1.25], [0.375, 0.225, 0.35, 0.22, 0.42, 1.27], [0.375, 0.475, 0.375, 0.34, 0.33, 0.98]], 'target': [[1.2, 1.2, 1.45, 1.41, 1.42, 1.33], [1.625, 1.35, 1.5, 1.35, 1.38, 1.32], [1.175, 1.2, 1.275, 1.23, 1.18, 1.43], [1.05, 1.125, 1.225, 1.17, 0.99, 1.38], [0.875, 0.925, 0.85, 0.93, 0.84, 1.38], [0.8, 0.725, 0.675, 0.85, 0.96, 1.3], [0.4, 0.55, 0.7, 0.6, 0.55, 1.18], [0.5, 0.45, 0.775, 0.4, 0.5, 1.02], [0.275, 0.45, 0.325, 0.32, 0.56, 1.13], [0.4, 0.225, 0.425, 0.25, 0.39, 1.17], [0.35, 0.425, 0.375, 0.33, 0.29, 0.92]], 'regions': [[1.95, 1.9, 1.725, 1.79, 1.77, 1.88], [1.425, 1.15, 1.425, 1.5, 1.33, 1.43], [1.0, 1.0, 1.025, 1.03, 1.05, 1.05], [1.025, 1.0, 1.05, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01, 1.01, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0, 1.0, 1.01]], 'sollength': [[0.275, 0.0, 0.0, 0.0, 0.0, 0.51], [0.15, 0.15, 0.0, 0.39, 0.11, 0.44], [0.925, 0.45, 0.25, 1.22, 1.38, 4.61], [1.725, 0.475, 1.075, 0.63, 0.24, 7.29], [0.775, 0.4, 1.275, 1.61, 0.46, 8.63], [2.525, 0.575, 2.275, 0.87, 1.45, 7.05], [0.85, 1.45, 0.4, 0.69, 0.9, 6.73], [0.725, 1.425, 2.375, 0.93, 1.17, 4.75], [1.275, 1.675, 0.875, 0.77, 1.47, 7.53], [0.625, 0.0, 0.675, 1.24, 0.79, 8.21], [0.525, 2.45, 0.0, 0.08, 0.72, 6.19]], 'iterations': [[1.0, 1.025, 1.025, 1.0, 1.0, 1.05], [2.175, 2.05, 2.375, 2.85, 2.57, 2.07], [6.475, 8.1, 13.525, 9.3, 8.19, 7.4], [19.725, 13.6, 18.35, 28.22, 21.16, 16.92], [54.25, 47.2, 38.275, 54.5, 62.0, 46.38], [110.6, 98.95, 81.8, 95.56, 79.77, 70.96], [214.15, 168.825, 167.775, 184.01, 183.16, 130.9], [202.95, 230.725, 258.525, 274.84, 277.62, 212.61], [414.7, 351.45, 396.05, 366.86, 370.9, 285.99], [442.8, 469.35, 432.2, 478.19, 429.28, 290.57], [549.075, 479.95, 541.35, 542.02, 529.55, 404.48]], 'changes': [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97, 2.0, 2.0], [5.0, 4.925, 5.0, 4.96, 4.95, 4.95], [6.825, 7.0, 6.7, 6.89, 6.97, 6.8], [9.85, 9.875, 9.95, 9.7, 9.85, 9.3], [11.225, 11.775, 11.375, 11.47, 11.5, 11.02], [13.95, 13.75, 14.575, 13.97, 13.87, 13.38], [15.65, 14.925, 14.625, 15.15, 15.35, 15.46], [15.55, 16.575, 16.6, 17.1, 16.8, 15.93], [17.225, 16.875, 18.025, 16.82, 17.26, 16.54], [18.2, 17.8, 18.45, 19.62, 18.84, 19.66]], 'player_const': [[0.5, 0.45, 0.275, 0.36, 0.38, 0.45], [0.375, 0.575, 0.45, 0.43, 0.47, 0.59], [0.7, 0.675, 0.675, 0.74, 0.68, 0.87], [0.675, 0.575, 0.65, 0.67, 0.6, 0.96], [0.575, 0.525, 0.475, 0.66, 0.47, 0.95], [0.475, 0.625, 0.625, 0.61, 0.54, 0.91], [0.5, 0.475, 0.525, 0.55, 0.44, 0.92], [0.475, 0.5, 0.475, 0.44, 0.62, 0.94], [0.525, 0.5, 0.575, 0.47, 0.41, 0.86], [0.425, 0.4, 0.4, 0.43, 0.48, 0.97], [0.425, 0.6, 0.45, 0.32, 0.38, 0.96]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17, 0.22, 0.36], [0.2, 0.125, 0.3, 0.31, 0.26, 0.61], [0.45, 0.4, 0.475, 0.41, 0.41, 0.85], [0.525, 0.375, 0.475, 0.52, 0.37, 0.84], [0.475, 0.375, 0.45, 0.43, 0.33, 0.88], [0.3, 0.3, 0.35, 0.27, 0.38, 0.8], [0.2, 0.3, 0.225, 0.24, 0.25, 0.7], [0.3, 0.3, 0.4, 0.14, 0.22, 0.54], [0.225, 0.225, 0.225, 0.19, 0.23, 0.72], [0.225, 0.125, 0.275, 0.12, 0.19, 0.66], [0.275, 0.25, 0.225, 0.19, 0.16, 0.58]], 'has_sol': [[0.025, 0.0, 0.0, 0.0, 0.0, 0.05], [0.025, 0.025, 0.0, 0.03, 0.03, 0.06], [0.125, 0.025, 0.05, 0.11, 0.1, 0.43], [0.125, 0.075, 0.075, 0.08, 0.04, 0.68], [0.075, 0.05, 0.075, 0.14, 0.06, 0.73], [0.15, 0.125, 0.175, 0.08, 0.11, 0.66], [0.125, 0.075, 0.075, 0.05, 0.07, 0.6], [0.05, 0.125, 0.15, 0.06, 0.11, 0.45], [0.075, 0.075, 0.125, 0.05, 0.1, 0.59], [0.075, 0.0, 0.075, 0.06, 0.05, 0.54], [0.1, 0.15, 0.0, 0.02, 0.06, 0.5]], 'success': [[0.0, 0.0, 0.0, 0.0, 0.0, 0.01], [0.0, 0.0, 0.0, 0.02, 0.0, 0.0], [0.0, 0.025, 0.0, 0.02, 0.04, 0.06], [0.05, 0.0, 0.05, 0.0, 0.0, 0.12], [0.0, 0.0, 0.025, 0.04, 0.01, 0.15], [0.075, 0.0, 0.075, 0.02, 0.04, 0.15], [0.025, 0.075, 0.0, 0.02, 0.04, 0.14], [0.025, 0.05, 0.075, 0.03, 0.02, 0.07], [0.05, 0.075, 0.0, 0.03, 0.06, 0.21], [0.025, 0.0, 0.025, 0.04, 0.03, 0.23], [0.0, 0.1, 0.0, 0.0, 0.02, 0.17]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_wide random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'sokoban_random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} hdiversity\n",
      "sokoban_narrow {'sokoban_narrow': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]], 'sokoban_turtle': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]], 'sokoban_turtle': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.426, 0.4115999999999999, 0.3896], [0.438, 0.3972, 0.41479999999999995], [0.395, 0.3632, 0.3628], [0.368, 0.3396, 0.33640000000000003], [0.305, 0.2932, 0.27959999999999996], [0.324, 0.28359999999999996, 0.2964], [0.31, 0.27359999999999995, 0.27599999999999997], [0.28900000000000003, 0.27759999999999996, 0.2736], [0.30500000000000005, 0.26280000000000003, 0.27280000000000004], [0.29400000000000004, 0.26, 0.2668], [0.29900000000000004, 0.27520000000000006, 0.28119999999999995]], 'sokoban_turtle': [[0.433, 0.39519999999999994, 0.38319999999999993], [0.4109999999999999, 0.39759999999999995, 0.4012], [0.421, 0.36479999999999996, 0.3624], [0.37, 0.3544, 0.34600000000000003], [0.34500000000000003, 0.3332, 0.32159999999999994], [0.36500000000000005, 0.3236, 0.3072], [0.347, 0.31720000000000004, 0.3236], [0.378, 0.31200000000000006, 0.3188], [0.35, 0.3112, 0.31079999999999997], [0.34500000000000003, 0.31079999999999997, 0.3104], [0.34400000000000003, 0.31679999999999997, 0.3303999999999999]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993, 0.41119999999999995, 0.3888], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995, 0.3767999999999999, 0.40639999999999993], [0.354, 0.365, 0.358, 0.33559999999999995, 0.34119999999999995, 0.36839999999999995], [0.309, 0.321, 0.329, 0.2964, 0.2712, 0.3324], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972, 0.1852, 0.28559999999999997], [0.146, 0.161, 0.182, 0.156, 0.17359999999999995, 0.2396], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999, 0.10039999999999999, 0.1928], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001, 0.0792, 0.14519999999999997], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005, 0.064, 0.16080000000000003], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001, 0.06, 0.1668], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376, 0.043199999999999995, 0.13200000000000003]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_wide random': [[], [], [], [], [], [], [], [], [], [], []], 'sokoban_random': [[], [], [], [], [], [], [], [], [], [], []]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+x/XXX8+1117LggULCAQCPPTQQ1x11VXjLYff/va33HjjjaRSKZYsWcK3vvUtwqHgxM0///UEPPv8C5xRN8U26VE4+FSuZfDEuG9UmT6yjWeRbDqXeP25DEcWuwHZ410303cCF4uLJ9lPpP8Zgr1b8B3Zgjm4O7fCD81nu62K1hXQePrkp70CYjswlMwwmMiQsU8+xlRo+hIOjx7I8vD+LJsP2iSyCq8prFgQZnVXA6sWNdAYmbqlYRjQFgtQG5rCh+04bnxo9DAoB9uBVNYmlXVIZtzXmV6PREbR33uQthduZ0nvvaTEx/96/4RvZK5id8JHfjzUb0JnzKArZtKVe10UM6gPyOTWtLLx978wLhb+/m2IclC5G690XDjzG28hGTnk3rT3Pgb7NuX6e7gCxskK2HFQdpb4vq3Yex7FOrARf/8LCA62J0SicQWjTSuJN51PNtQ8/cHyyQnFGWeefeq1HJRSZJ0stmNj/vYjGAefPm7RKHXMzPTYTWeSuPLjU50YB7jx4zfzzNZnuP/hDfzh/gf40ue/QDrrIAKOgrSTIZ11M2Xq6+vZvHkzAN/85jendCP1HDnMJz/5Ce7+za8JBQN8+jOf5d8/+2k+9pEPz9zoo8kk4NDTrhjsexzVk/sTGhaZ+tNJnPnnDNedQ7L2NNQJb9CCY0WwvRFsXxQMC8kmseIHMdJTl0rP+mvpb72E/tZLADBTAwR6nibUt5VAzxasx77pXk7Th7Sc5bYqWs+DptNnF7SbgrTtMBjPMJTMUOBkjpNCKcX2foeH92d5ZH+GbX3uDboxIFzR5eGiNg8rmjz4PQL0QnoIZ/BYt1TQZ9JeG8DnmeLJMtHvxobysrRMA4Jek6DXZCzcPiYYyazrikpmbbL25ItlpgbpeP4Ozt75S0AYXPoa+pa9gdW+GKuBjK04FHc4MKLYN+y6qPYM2mw8kOW3OyeOFbZwBaPGYNG4cCwnevrp9J3+Foz0CIGepwgd3kzw8ONYux90r1e0baJVcTyXzcmQScCBJyfcPWNu1GADdF2Wc32d72YLFZi07TCUyDKczJD1L4HlS2D5WzDSwwSPPOm2sA4/TvjABnf7UBvxpvOJN60k3nguyjPNaHsqi5nqm7VdVSsOWdthIDVMb7yftJ0hkUkDCq+jMCvgJqBUrtWhFI6jiGfSKKV4zeteTyprYyBuLMTOouwMMtYKyCZ5+A8P8Mwzz3LJZW79pHQ6w9qLLpylATl3UmoE9bN/hCPPIU4WJSbpuuWMLnsj8cZzSdadPr3vWzyuGHgjON7oMdk4yuMnHe1Csgms+KHjisQYtq+GkQWXMrLALRZopAYJ9LpCEex9Gt/GW93jml5U05kYbSvdlkXTGTPOdomnbQYTGUZSlTOqXiKrePxgNicIWfqSCgHOqDe5+hwfF7V5WFxjTB2nOsotJUBdOECtL4qMBFwXhxVwW2N2yq2amp5ZHa/JguGSdZTbwogPY239EaFtP0GyKYYWXknfaW8hG5w8lrtlCu0Rk/YIrG6dfPzBlMOuwbHJZteAw+93Z/hFZiLAUecXumIGi2o8dMVW0dWxhoVnCtH0AffmeGgzwW2/wXjmZ6hcsFfaV0H7hblY1iwzlZTjFhEcc+kc3OKmyeaC5pz+KleIChE0n+r0CkbSWYYSWeLpqX+jjjcy8T9RCmukOyeam4nu+R01O3+BEg+J+jNcoWg6n1RsccGy5apOHFIZm/t3vMhwepBsrt5Lu/Jh57oCJq64uZzmYXpMnLxH1FQy5+9XrpPLsgQ7nci5rhyymRSpVNJNP1SKrO2QdRyu/KOX8N/fuw2Dmf4wFWQSqEzCdRllkwgK0qOkUiniS15DouFcEvVnTv+kASjDh+2L4lgRHCs0oz+I8gQmRGL00GT/+glwfDFG2y5mtM31wxrpIQI9Wwn0Pk2g52l8m25DUCjDwm48A1rPw1ywEmk+c5JYKAXDySyDiTTJbGW4jg6OuK2DRw9keOKQTcaBoAWrWtzWwepWDzX+2f2ZLdOgOeojYJm5zLP8fPyx7+nknpA8ThrPtjsJPf4912e/aB3ZC/4KM7yAWMYhmXFIZW2yM2iOxXwGK5oMVjRNLFNK0ZNQ44Kxc8B9/cX2NPl9uFpCUbpiV9AVeylLznI4T55n0eiThI9sxr/xVth4K8oXRRZckHP3rHKD0VMR75uIG+zbODFAUd1iOPu1rtAUMt0WmRDtbAoyo2SyNkPJDEOJzIyu3cShhEykg4FIBwNLXoPYGfx9W90g/6HNNDzzHXjmO2S9URJNKxnNtSxs/9wLGFadOKSdNP3JnnKbMYlwOMzIsFuDpqOjg23PPUcqMUoykeC+e+/lootWI47bskFlyTgpLPEQydtP5SZbKS5YtYr3X3c9z257gSVLlpCIx9m/fz+nLV82EeRToLJJJBNHMnGMbIKxMLFjeMlaUTJmkLhP8cszP4utXBeXfVjhqMzEe+W6pW2lsA0vGcNP1vBjiwfbcXDUALYzgKMUtqPyXsl1ulG0RP10NYRYWB8i7PO4IhGbvUiM4XijjLatZbRtLYDrasi1LAK9T+N74nvIE99BGR4y9adjN68gW7eMYQmTttwgHJ7i5X+fCNtRPNtrj7cOdg26ItUeMXj1Mi9r2jyc3WBimXOzLer30Bj2n6Df10k2m50svPA72PgtN06x4AJY/S5oOh0PEAbI8/RlxloYuXIfyYw9fTowICI0BoXGoMHq1onbkO0oDo4qt4Ux1tIYdHjsQBbX07UYUxbTHnk959SPsN7zNOdln6Cz+wm8O37vXoGaLmQsRdSwJlxFvdvdk/hjsGBVzlW0CkINJ3fNxj+UAVYIZQXJWiHSEiDtQDprM5DN0jeaYnh0FJVJoNIJVDaBk824/ymlcv/DvP+lUtj5y8b+p+PbnInjPRN7wZ/jb+qna+RJukafZMmBJ2jpvg+AA1Yn2wLn8Zxvxaw/TtWJQ7nJOBlSdhK/6cMjHlCK+tooF110IRdfsJorX3oFr33tq7n4wrUsXLiQc889Z9L+NpB1IK2yvOHNb+G9f3sdfn+AX9z1O7I2DCQVHcF6Pv35r/CWt/8V6VQKBXzgHz+Kp2EhQZUgzCgRElji3niSymKECKMqwAh+bMeAXEu1Lwkf/L+ZDvCeBKa/kRu5sgyGuHKUzntKbwj76KoPsqghRFd9iK6GJtojjfiTR2YtEmM43jCjrWsYbV3jnj8zir/3GYI9Wwj0bMH/9O0IDvkDTzqGB9sbw/bFcq/RiffjyybmZ9o6yieVdTN69g45dA+7N7JNB22G0wpT4JxGk2tX+ljT6qE9enKdA00RGqM+Ir4i/WWVgl1/gMe+6Ra8azwd1n/IFYcTYBmC5fUQzhcMW5GybWzbvcE5OdfqxA1QuS5Vx53ypcQ0hAURYUHE4JL2ycfsHnbYmScYmwZC/GL0QuBCQHGOuZdXBbZwWfwplj/9Uzxb/gcAR0wGY2dyZNHb2V9zPkcCi0nbQmpUkXkWUvYgGVuRtiFtK1K2ct877vu0zfHfO0LGwV3mKLeoou2UoSpVEFgLrEVwOFP2cLn5FJc7T3Fx+peslzt5zyyPWLRsJRG5FXgVcFgpdfYJtlsNPAS8WSn1o+mOe9o5Z6nP/PDWScvasz6WLFs6rU0TWUd579XkdSi3t7O7kRs3cFSWpJMg5cTJqjE/qRCUKBZBHAVKCY4CJ3d8R4GjJG9+Zs90gnvzNUQwBPySJqziBInjV677ycYkbQbJeoKkjQCOePI/wbhjQQR2vvgiO44MuDd0AcPwIN4geMOIL4RhmG59HUMm3fSNsTIJBuNlE8ZsyveJK+UWpNvZO8qunji7e0fZ1TvK3v7EeHd+jyF01gXpqvOxJJJlSSTL4hqD2lm6U46HkYljjezDTA9ipnLTpPkhPKkBzPQQxnFKKivx5AQkOkk8Mt4YgxLloB1hTyrCzlSYbaMRnh/xcyghk77TpqCwosnDmjYPq1o8hLyFabkEvR6aoj6sYlVi3bcZHv0GHHnWLXmx+q/dIGyJWl5KuYLhjIuHg+OM/W8mWqxOnriMCc5IymH3kMPOo1oao4kkFxrP4cHmEecMRpnelQruf8QywWsIlumKn9ckVx1XsDwmXo8Hy7KwLAufKXg9Jl6P++rzGPg8xvgyn8fEMg0yttuyEsn/P03838zx/xwYysajUnicFB47haVSGOK46yS3jbixIvd/nVtmuPaPrR/7n471M2m/6oOzylYqpjhcDowA3zmeOIiICfwO95H11pmIQ6RjuTr3fV/Ezt2/bUfxmSuaae5cMr7NpDR/924/ByVXiJlAzDhiuKmayrFQThBsH2INIkYK5fhxMrWIMnJf/NgXA4YoDMhbribW4X6Zgvtlek2P+wNBYWQTGJlRjOwoRq73pjJ94A0hvrAbdDxOLMIhLxCuYNvz2/EbabLeKI43ilMid0vGdujuT7Crd5RdPaPs6o2zq3eUvtGJzJkan7CoxmBxjZvmuLjGZGHMwDtHl8tMEDs9hXgM4sQHSY4O4CTcZb7MICFniDBTi4mNQdyMkrGiKH8MM1gD/hrS4XZSsS7S0S63NXIytgL1YR+1wSL1tj+yzRWFfRvdOkUXXA3LX1ZRHRVnwpi45ItIf9xhW2+GwZTC55HcTRx8puA3Ba/H7f0d8LjVbf2mgc90x9oYx/S6Y117w252lOU/STvdcu/98TQjqezMCzkqhdgpjGwcIxPHyMYRe5adWoHlKy+vjFRWpdT9ItI1zWbvB34MrJ7pcU1DqAubbsXJnDp6DMFvybjPPeeVz3uCHlt+1NO15D1l4wpJlhRpEmSUe/ENTHwSxicBTNMzfl8VakmqUYYZxvIfos6qwW/MLe1SHBtPehgrm8TMxQ5ULpilgvWIN4TMsCy4MfahDBMME4/lZekZK8bz2pM5v3Ayaxe19LRlGixqCLGoIQSnTSwfTGTGWxe7jwyx68jQpCCkIa5/flHMYFGNyeKceDQFZeosnlmQdRQHR032DtfQPRxl79ACuoddl1B+By9DoDVs0F5jsDBssywwQpd/mA7PMDE1hCc9NC4u3jGRGXwR83A/Zl7LJBNoJBXtIh1bRCraRSq2iEyobUaZNT6PQXPUj89ThDpNA3tg461upytfFC56L5z56gIGYkuLCHhy94ExQl5or5nl/9Hjd0VgTBA8J5dGfaydQixoEQtaZGyHgXiGgXia5AlKs+d2RHn82B7/RIDZsd2HyGw8JxoJUJkTH2eWlO0RQUQWAK8FXsI04iAi1wDXADS3NnPtRWlwsu5TtcoS9tbQEJj7hUk5aeJ2goSTxFEKQwzCZpCgGcA76aY8+WbqI0TA9NKXGaQn3UfYEyJmhmd0EzPtNJ5sAk8mgcdxn6YdMXF8EQxfBLGCs09JMwwQT+41t6+4N1W/ZeK3Jt+U7FwwcVwwcuJR6OqO+cQCFue213Buew3gdmxSqRGOHN7Prp5hdgy4qY7P99nct3cixS9oweKYyaKaCdHoipmErMnXWinFQEqNxwG6hydiAgdGHPLT92t8QnvEYE2bh/aIQUfEoD1q0BoyjgoYRwA3P/OEQ98ohSfZi3dwJ76hXfiGduEd3Eno8CYk11nRMSzSkU7S0S5SsS5S0UWkY13YvpoJuwIW9SFf4StSjxyGzd+Gbb92n4rPfwec+6bC9xkQM1eBVNyCcY7tpo462dx8bllZx4sQN4soXwwKMO7HTLFMg8aIj8aIj0Taraw6EJ9FZVXDxPGGcbzh8UVip5FsPOd5iOdcqHPP2itn+/HzwIeUUs50N1Ol1C3ALQCnn7lMmamBkz552smQcJLE7QS2chCEgOkjaAbwGb4ZJ5B6DYtmbz0D2WFGsqOknBR1Vg2WTL60ohzMbBIrm8CTTWIoGwU4po9MoBbbCriFyQBDDLwi09sguH9Ew3RfZ/lkbRpC0OsheNQDUsZ28loaE+JRrM704gvT1LGclpZRLo0fwsi4t+B4xs1a2THguKIx6PB/uzOMbp94EGgJCYtrTAIeYd+wzd5hh9G85wTLgAW5lshlHR46IgYLIgYdUZNIgWICEx9EyAYayAYaiLdMPO+IncEa2YtvMCcYQzvdXPW994xvk/XVkIl14WlcitW41K1wW9NZmKf55CA88X3Y+r/u/fis18LKv3CHqCwkhpUba6F+Zv0OnJxgjAtI7nXS/Nh6J/eaJzCzQnJCEJpwE1VIBeGA1yTgDdAa88/N7ZRDmV6U6cUZe9BQCrGT466o2VJOcVgF/CAnDA3AH4tIVin102KdMKts4naCuJ0kq9ynUr/hI+YJEDB8c3ZbiAi1VhS/4aM/O8jhVC8xK0JUvBOtAzuVcxcZ2JafrDeIbQUmFSgbw1EOKSeD1/BgTNXXWwz3KWcOgjATxkYWCx+VGTPWyhhLXRwrv1Ao0XCsEOnYYoz0CJ74IYKMcmaDhzPzMg2VUhyJK3YMuKKxMyceiayiPWLwRwstOqIG7RGTjohBY3COI6IVEGVapGOLSccWk98tzUwN4h3ahW9wF6HR3QSGdyHP3Qlbc3EZMVyBqF0E9UvcfPz6JW58YCbfeyYOW34ET/7QnV/+MjeuEGmddtdZYfrmNpSnYcAcXbHAhJBMEhhn8jLDk4sXhIo/ONBJMme30/EPiPIEsD0BbGY/rkbZxEEptWhsXkRuA34xE2EYG6DdyeXaT4ejHOK5FkI612nOa1jUeKIEDX9BB+cJGF4CZoTe7DADmSFsx6Etm8UwLLK+CI43iOPxcbxgcj5KKVJ2Bsvw4BkTEMOTm8rzI/d5zFyZhglXm1KKVNYZL7swJhjpkxANxxsm7Q2Pi4SRnejkJSI0hYSmkMFFJS63U2hsX4xk4woii9cQDOT+ik7WLQHdt9Ptwdu3w80iyuXwA+7Nri4nFmOCUbvILeoG7pgCz/4cHv+u29Fr4SWw+p3utoXECrothUK3QGZKLq42qePFPOGk3U4FoGjiICK3A+uBBhHpBm4kd1dRSn1trsf1GAatsYmsAbdlKm6WQe66OUoRt5OM2gkSuaJwluEhZkUIGP6Jm20BEOW4rYNxd5FDFOixfBwxDLb7/DT6avnz17+N2277BjU1kzMePvnJfyUcDnHdde+f8vgZZeMYHiyPvyKHIc2PZ8SmEI0xt9RYqyM9i57LJxKJ+YDfY9Ic8+E188Te8EDNQndavH5ieXrEFYy+He7U+yK88Fu3RTBGpA3qFkHfi24Z6dYV8LJPuuMKFBJvxBUFf7Swx9VMySS3UzJL/+jc3E6zpZjZSm+ZxbZXz/U8huFqggAJJ8VINsGonUQphUdMYlaYiOfowHJen4e8PNfJfR0Y7+vglr5Qbv2j3Ksnm8STTWDaKTfTSQxsK0jW6zbjwmLgdTIcSvVzMNnLf93xDWLeWfyZ8oLLNqDsNJZpVc0wpMcLgjtOnmjkFXo7UebUhEgMY8UPIXPwn1YadSEvdUHvzL0w3rBb2qElr1OlUjByEHp35InGdgjUwaXXu+UkCvlA4YtCpKXwAWzNjBARYgGLWMAiazv0n6zbaRqqK6EZt8NTeN8TGE4GshmeS/cSWvAO9owexMbtVxAVk6iYBBAkk4aM23qQvJs9eTf7iV5xyk13HVs/jS2O6SXrj+F4g/zbF7+Oz+fjb/7m3fzjP97Ali1b+fWvf8b2R57lG9+6jUcffoyf3PNjTm9bzH989ot8//s/oLGxgfb2Baxc6XZt37FzF9dd9w/09PQSDAb5yte+wmmnu3mgDg5pO41lWNUzDOkUGIbknoROnDk1JiD51UEdb4SUN1LVIuExhZaI/5jPPydE3PhBpBW6Ljn54019EgjUuDEFa2YdyTTFx1MCt1PViYM1tB/74a9wZyjEr8JBui2LL7T9BSHHJuY4fPXZ77BteE/eHkelOsIUT1MyxduJZctji/n7c96d209QIm5mQF5noUsuWcsXvvAV/uZv3s3mzU+QSqXJZDJsePBh/uiydWx6ZBOOcvjtw/fxw//5EQ8/fB/ZbJaLL34JK1euBNPife+7ni9+5YssXbaURx95lOvefx2//t2v82xXpJ00FhaeKuuoNB3Hy5yyHTWeNTXe4jCiOZEYwoofrhqRiOTqIpnV0PgTw22BhJuqtv/DqcLRbqeBeJrh5Mm7naruDvOiP8hr2tswEM4JLeBPa06n3orQEG4Dwa0kmji5noxHo0wvti9ywm1WrjyPxx9/kqGhIXw+H+eddy6bNz/Ohg0P87nP/Quf+5zQ6q/nV4/8kvVXvYQhM0FjqJ5XvuqVYJqMxBM8/NDDvPXNbx0/ZjqdnvJcGSeDo5xphyGdD5iGEPJ5xofBHCNrOySzIZKZJtKjA2RH+shkM2SzNpLLWhE1lvJYznx6t1NdU8RPxF8Ffzcx3UJ0oaaS5v1rTp6j3U4DiQz9o3N3O1Xfty8Gb+98BWvrz6bW696w7bQ53unr+pV/WxazLMuiq2sh3/ve7axZs5pzzjmL++77Ay++uIPTc64hU0yiVphRT5yEnaY7fohMLqXWcRxiNTEe2fTIjM5nKxvbtjEwMMQdA8AQo2piEieLxzQIj6Xbhpuh2R0daywQnrbd4Hc665DOZElnMmQyGZQzIRqinJyQuPPuq+26F8e2Gcuvn2NnooBl0hz1z7kKa8kwLAg1usJQxW5LjYvHNGgI+2gIT7idZn2MIthVVNqDLfxx69pymzElF198EZ///Jf52te+xFlnncmHPvRRVq48b+Lp3vRwybp1vPud7+Yf/+lDHBg6wK9+8Sve9s63EYlE6Orq4ic/+gmve8PrUEqx5aktnLvi3BOe08HBUc74w7EgE2Jx3PHw5i/HC4SPkckXjdz8WOrtCf21SuWJx9SiIo7t9lK1kxhOmrqwG3SuaExfLh21ruL7AWjmxpjbabZUnThUMpdcspbPfObfWbNmNaFQCL/fzyWXXuzWaBEBw2Tl+St5/Ztez2UXXkZjYyMrL1hJMptk38g+vv6tr/P3H/h7Pv2pT5PJZnjDm94wrTgcjUJhK3tcLDJ2hh2DOwh6ggQ8AYJWEGuGdZrmI2Md/EJTuNFtR433Dk8f1frI2A5KeVC5v8yJHFU+y2BBzEfAyEAmCdlE7jXpjjZWCXgCE30U5rlrUjM3ilaVtVicc85p6gf/8y+TltnphSxbVuAOPlMgIphiYGC4bh11HFdDTghm2oM5nolzJHEERznU+euIeWMz6Sc3I1547gXM1slP0ZZhuUKRE4yAJzDvYxcni1LqKLFQOQFxA+W5gQipC3tpjfoxjtcr2866YpFNueMWZ8dEo0TDmXrDuT4KsdKcT1MxiEhlVGWdL4wJwpgojGGKga0cMirLuMCKkddrc+YErSDtZjtHEkfoTfQSz8ZpCjQVLWU142TIpDMMpcfGI5ZxkdCti6kRkbwe4sfiDu+qjuvOGsf0gBmBoxMc7MxksRhrcRzvAWS2+KJubMYXnn5bjYZqFAfDdDMpnFyQ0HEgI3kVTPMHc5hbq+h4gnA0phgY4iULZIWTap6bhklLsIXB9CB9yT66h7tpDDYStIJzPuZMUSji2TjxvJRQ3bqYHR7T4Di6MTNMy504qqNkNp3nlsq1OLLJGYqGuC2EcPNEaQ2NZoZUpzgEj6rlErdR5nHSOvOHe5vo+py3fmIMiAlBkPydpxYZETA8iOHBEsFUDhk7g3MSJXIRiPliBDwBDscPc3D0IFFflHp//ZxuzEqpObunjm5dGBj4Pf7xlkXAE9Cti1Lg8bpTvhtIqQmRyCbzWhwpxusFBOtcUdB9FDRzpPrEYSo8MNA3QE1dzbE30Ukd2mTSzVIQTDExDXNmKaBjParhmLEWDDHwml6yKkv2JP3HXtPLgvAC+pJ9DKYGSWQTNAeb8Zozz3xRSjHQN1Cwb9jBGW9d9CZ7Abd1EbJC1Pvr8XsK27dEcwJE3FHJjh6ZTClXJAxPrhWi0cydeSEORq1BT38PPT090/d3Eib1DSgGSrkZQ4UI9qedNIfTh+lW3eNP7DMY6cEVQY97bYpFxskwkBpgIDVAzBujKdg0KwHTFBgRXeJCUzDmhTiIKZgNx3f4+k0/UW+UqC+KzyxdM3swNcjB0YPjY0fMhQABMqkMX3/q62w6tIkVjSt4z4r3UOOvKZyhBWAwPchQeohaXy0NwQbtctJoqpzqS2VdeY76wd0/mHa7cgnC0diOzaH4IfpT/Sd1HKUUv9v9O777zHfxe/xcu+JaLmi+oEBWFhYDgzp/HQ2BhqouEqjRzCdmm8o6r8ShUgRhKuKZOPtH9pNyUid1nH3D+/jS419i19Au1rWv4+VdL2dRbFFFZhKZYtIQaKDOX3fKlPXQaCqVU04cAmaAqC9K1ButeH+3UoojiSP0JHrccSTmSMbOcMe2O7hr111knAydkU7Wdazj0gWXEvNVXucmj3hoDDZS66utSBHTaE4FTglx+Nnvf1Y1gjAVKTvF/pH9k/oVzIXRzCgb9m/g3r338uLAi5hisrJpJes71nNe03kVV9bba3hpCjZVpIBpNPOdeS8OF6y6QG3auKncZhSE/mQ/h+KH3FpIJ8ne4b3ct/c+Htj3AIOpQWLeGJe2X8r69vV0RDsKYG3h8Jt+moJNRLwnLoOu0WgKR8WIg4jcCrwKOKyUOnuK9W8FPoSbdDkMvEcp9eR0x121apXauHFjoc0tGxknw6HRQwymBwtyvKyT5ckjT3Lv3nvZfGgztrJZElvCuo51XNx2MWFv5ZRPCHqCNAebS9ILXKM51akkcbgcGAG+cxxxuBh4VinVLyJXATcppdZMd9z5Jg5jjKRH2D+6n0wBq3YOpYb4w74/cO/ee9kzvAfLsFjVsor17es5p/GcigkSh60wTcEmAh6do6/RFIuKEYecMV3AL6YSh6O2qwWeVkotmO6Y81UcABzlcDh+mL5k30kFrI9GKcWuoV3cu/deHtz3ICOZEer8dVzefjnr2tfRGm4t2LlOhqg3SlOwqeIyzTSa+UC1isPfA6crpf56umPOZ3EYI5FNcGDkAAk7UfBjZ+wMmw5t4t699/LkkSdRKE6rO4317eu5qO2isj+9C0KNr4bGYKPuSKfRFJCqEwcReQnwVeBSpVTvcba5BrgGoLOz84Ldu3cXwdrKQilFX7KPw/HDJ1fM7wT0Jfp4YN8D3Lv3Xg6MHsBn+ljTuob1Hes5o+6MsqadGhjU+mtpCDRUXNaVRlONVJU4iMi5wP8CVymlnp/JMU+FlkM+GTvDgdEDDGeGi3YOpRTP9z/PfXvv46EDD5HIJmgKNrGufR3rOtbREGgo2rmnwxSTen899YH6iomRaDTVSNWIg4h0Av8HvF0ptWGmxzzVxGGMQtRpmgnJbJJHDz7KfXvvY2vvVgTh7IazWdexjgtbLixbvxKPeMZ7W+uOdBrN7KkYcRCR24H1QANwCLgRsACUUl8TkW8CrwfGfETZmRh+qooDuHWaDscP05fqK8n5DscPc9/e+7iv+z56Ej0EPUEubruYdR3rWFqztCw3acuwaAq4Hem0SGg0M6dixKFYnMriMEY8E+fA6AGSdrIk53OUwzO9z3Dv3nt59MCjpJ00C8ILWN+xnssWXFaWCrE+w0dTqImoNzr9xhqNRovDqYJSit5kL72J3qK7mvKJZ+I8tP8h7u2+lxf6X8AQg9cufS2vX/76ssQEAp4ATYGmiurcp9FUIlocTjFsx6Yn0UNfsq9oWU3HY9/IPv73hf/lD/v+wMqmlbz3vPeW7SbtM3yErBAhb4iQJ6RLhWs0R6HF4RQl42ToiffQn+ovaAe66RgbZ+LbW79NfaCeD676IAujC0t2/uMRMAOuWFghglZQZzppTnm0OJzipO00h+KHGEoPlfS8z/c/z39s/A9GM6O869x3cVn7ZSU9/4kQhIAnQNgKE7JC7lCrOpitOcXQ4qAB3F7Wh+OHGcmMlOycA6kBvrDpCzzb9ywv73o5bzvzbRXZgc3AGG9RjImFRjPf0eKgmcRIeoTD8cNFKcUxFVkny+3P3c4vd/yS5bXLue6C66jz15Xk3HPFI55xoQhZIV3bSTMv0eKgmZLB1CCH44dJO+mSnG/D/g18/cmvE/AE+MD5H+CM+jNKct5CYBmWKxQeN8Ctazxp5gNaHDTHRSlFf6qfI/EjJUl/3Tu8l3/b+G8ciR/hrWe8lasWXVWVvv6xTKix1kUluso0munQ4qCZFkc59CX76En0FGQUuhMRz8T56hNfZeOhjVzSdgnvOvdd+D3+op6z2ATMAEErSNgK60woTdWgxUEzY7JOlp5ED/3J/qL2kXCUw8+2/4w7tt1BR6SD61ddT0uopWjnKyVjmVAhK0TEG9HBbU3FMltx0I88pzAew0NLqIWlNUup9dUiFMflY4jBa5e9ln+68J/oS/ZxwwM3sOnQ/BgHXKGIZ+McSRxhx+AODo4epNoeuDSaqdDioMEyLdrCbSypWULUKl6tohVNK/jUZZ+iKdjEZx/7LHdsuwNHlbZXd7HpTfaya2gXGbtww71qNOVAi4NmHJ/poyPawaLoIoKeYFHO0RRs4uOXfJx17ev4yQs/4dOPfpqRdOn6YpSCeDbOjsEdDKeLNwaHRlNstDhojiFoBVkUW0RnpBO/Wfjgsdf0cu2Ka3nnOe/k6Z6nueEPN7BrcFfBz1NOsirLnuE92s2kqVq0OGiOS8QbYUnNEtrD7XiNwg7yIyK8dOFLuenim8g6Wf7fg/+P+7vvL+g5KoHeZC87h3ZqN5Om6tDioJmWmC/G0pqltARb8Ehhc/yX1S7jU5d9imW1y/jqE1/l1i23knVKV4K8FCSyCV4cfLHk9a40mpNBi4NmRogI9YF6ltUuoynQhCmFK4ld46vhI2s+wisXv5Lf7v4tNz90M32J0ox2VypsZbN3eK92M2mqBi0OmllhiEFjsJGlNUup99cXLP3VNEzedubb+MD5H2DP0B4+/MCHeab3mYIcu5IYczOl7dKUMdFo5ooWB82cGOsjsaRmSUFdTWvb1vLJSz9J0AryyYc/yS93/HLePWknsgl2DO7QbiZNRVM0cRCRW0XksIg8fZz1IiJfFJHtIvKUiJxfLFs0xcNn+lgYXVhQgeiIdPDPl/4zFzRdwHef+S5ffPyLJLOlGS+7VGg3k6bSKWbL4TbgFSdYfxWwLDddA/xnEW3RFBG/x09ntLOgcYigFeTvVv0dbz79zTy8/2E++uBHOTByoGDHrxR6k73sHNRuJk3lUTRxUErdD5woqvga4DvK5WGgRkRai2WPprgEPAE6I50YBfxJGWLwp0v/lA+v+TCDyUFu+MMNbDw4/+pqJWzXzTSYGiy3KRrNOOWMOSwA9ua9784tOwYRuUZENorIxiNHjpTEOM3sCVpBOqOFFQiAcxvP5VOXfYrWUCuf2/g5fvjcD+dd2Q1b2XSPdHNg5IB2M2kqgqoISCulblFKrVJKrWpsbCy3OZoTELJCdEQ6Cl7ErzHYyE0X38T6jvX87/b/5dOPfnpelqfoS/VpN5OmIiinOOwDOvLet+eWaaqcsDdMe6S94ALhNb28+9x389fn/DVbe7dywwM3sHNwZ0HPUQloN5OmEiinONwJvD2XtXQRMKiUmn8Rx1OUqDfKgvCUXsKTQkS4cuGV3LT2Jmxl87EHP8a9e+8t+HnKTb6bab650DTVQdEG+xGR24H1QANwCLgRsACUUl8Td7zIL+NmNMWBv1RKTRtt1IP9VBcDyQH2jRanQTiYGuSLm7/I1t6tXNx2MS/pfAln1p2JaRQua6oS8Jt+2iPt+ExfuU3RVDEFHwlORBYppXZOt6xUaHGoPvqSfRwYLU6j0HZs7nj+Du7aeRcpO0XMG+PC1gtZ27aW0+tOnzdDeBoYtIXbiPli5TZFU6UUQxw2K6XOP2rZJqXUBXO08aTQ4lCd9CR6OBQ/VLTjp+wUjx96nIcPPMzmQ5tJO2lqfbWsaV3DRW0Xsbx2+bwQilpfLS2hlnnxWTSlZbbicNxurSJyOnAWEBOR1+WtigLVPUK8puQ0BBpwlMORRHFSkX2mj4vaLuKitotIZpNsPryZh/c/zD177uGuXXdR569jTesa1ratZVnNMlyvZvXRn+onkU1oN5Om6By35SAirwH+FHg1bvB4jGHgB0qpDUW3bgp0y6G6OTh6kN5kb8nOl8gm2HRoEw/vf5gnjjxB1snSEGhgTesaLm67mMWxxVUpFAYGraFWavw15TZFUyUUw620Vin10ElbViC0OFQ/B0YO0JcqfUnueCbOxkMbeWj/Qzx15ClsZdMUbOKi1otY27aWrmhX1QlFja+G1lCrdjNppqUY4rAct+5Rs1LqbBE5F3i1UuqTJ2fq3NDiMD/YN7KPgdRA2c4/kh4ZF4qne57GVjYtwRYuanOFojPSWTVCobOZNDOhGOJwH/APwNeVUitzy55WSp19UpbOES0O84fu4W4G0+Xv6DWcHuaxg4/x0P6H2Nq7FUc5tIXaXKFoXUtHtGP6g5QZ7WbSTEcxxOExpdRqEXk8TxyeUEqdd3Kmzg0tDvMHpRTdw90MZSpnXIOh1BCPHnyUh/Y/xDO9z6BQtIfbx1sUxejYV0hqfDU0B5vxGIUdzlVT/RQsWymPHhFZAqjcCd4A6J7MmpNGRGiPtLNneA8jmZFymwNA1BflyoVXcuXCKxlIDfDoAVcofvz8j/nR8z+iM9LJ2ra1XNR6Ea3hyisiPJAaYDA1SNgKE/PFiHgjOh6hmRMzaTksBm4BLgb6gZ3AXyildhXduinQLYf5h6Mc9gztYTQ7Wm5Tjktfss8VigMPsa1vGwBd0a5xoWgONZfZwqkxMAh7XaEIW2EtFKcwBXcr5R04BBhKqbKWwtTiMD9xlMOuoV0ksolymzItvYleHjnwCA8deIgX+l8AYHFsMWvb1vLShS/F76nMbkCmmES8EWLeGCErVDUBd01hKEbM4fopFg8Cm5RST8zOvJNHi8P8xXZsdg/tJmFXvkCMcSR+ZFwoXhx4kaU1S/nQhR8i4o2U27QT4hEPUW+UmC9G0AqW2xxNCSiGOPw3sAr4eW7Rq4CngC7gf5RSn5mbqXNDi8P8Jutk2T20m6RdfWNGbzy4kS9s/gJNwSY+vObDNAQaym3SjLAMa1woAp5Auc3RFInZisNMHJDtwPlKqQ8qpT4IXAA0AZcDV8/JSo3mOHgMD53RTryGt9ymzJpVLav48JoP05fs48YHb2TfSHUMT5JxMvQme9kxuIMX+l/gcPwwKTtVbrM0ZWYm4tAE5P9SMrgd4hJHLddoCoJlWHRFu7AMq9ymzJoz68/kxrU3klVZbnrwJrb3by+3SbMi7aQ5kjjC9oHtvDjwIj2JHj0q3SnKTMTh+8AjInKjiNwIPAj8dy5A/UxRrdOcslimKxAeqb58/a5YFzdffDMBK8AnHv4ETx15qtwmzYmkneRQ/BAvDLzAjsEd9CZ6yTiZcpulKREnjDnkBuRpB5qBS3KLH5zJoDzFQsccTi1Sdopdg7vIqmy5TZk1/cl+/uWRf2HfyD7et/J9rG1bW26TCkLIExrvQ6E721UPxQhIb1FKnXPSlhUILQ6nHslskt1Du6tSIEYzo3z2sc+yrW8bf3n2X/KyrpeV26SCIYjubFdFFCMgvVlEVp+ETRrNSeH3+OmMdmJK9Q3/GbJC3LDmBs5vPp9bn76VHz3/I4o1NG+pUSiGM8N0j3SzrW8be4f3Mpga1GNezxNm0iZcA7xVRHYDo4AASil1blEt02jyCHgCdEY62T20G4fquvl4TS/XX3A9tzx1Cz96/kcMpYa4+uyr59WTtoPDUHqIofQQppiErTBe04vH8LiTeLAMC4/h0Z3vqoSZiMPL53pwEXkF8AXABL6plPrXo9Z3At8GanLb/JNS6ldzPZ9mfhO0gnRGO9kztKfqBMI0TK5dcS1Rb5Sf7/g5w5lh3nvee+elz95W9gmr7ZpijgvFmHCMzecvn0/iWY1M+8tUSu0GEJEmZjE8qIiYwFeAlwLdwGMicqdSKj/D6aPAHUqp/xSRM4Ff4Xau02imJGSFaI+0s3d4L4rqcs+ICG89861EfVG+/+z3Gc2Mcv0F11dsuY1iYSsb27bBPvF2ppiThGMqAfGIB9OoPndjNTCtOIjIq4F/A9qAw8BC4Fnc8aVPxIXAdqXUjtxxfgC8hsnprwp3TGqAGLB/NsZrTk0i3ggLwgvYN7Kv6gQC4E+W/AkRb4RbnrqFTzz8CT504YeIeqPT73iKYSsbW9mknBN3pzIwphYP8WAYBgYGIoIgiAiGGBPzuXW6lXIsM2nTfgK4CLhbKbVSRF4C/MUM9lsA7M17340bv8jnJuC3IvJ+IARcOdWBROQa4BqAzs7OGZxaM9+J+WIAdI90l9mSubG+Yz1hK8wXNn+BmzbcxA1rbqiachuVhoND2kmTdk6us54wWTiO+36W6zyGh5AVKtCnLR0zSWXdqJRaJSJPAiuVUo6IPKmUWjHNfm8AXqGU+uvc+7cBa5RS78vb5vqcDf8mImuB/wLOVur46Q46lVWTT3+yn/2j1dvgfLb3WT7z2GcIeAJ8ZM1HWBCp7MGENHMj6AnSHGwua5HDYqSyDohIGLgf+L6IfAGYycgs+4D88RXbc8vyeSdwB4BS6iHcmIZ+fNLMmFp/LS3BlnKbMWfOqD+Dmy6+CVvZ3LSh+sptaGZGPBtn59BO9g7vrZpyJDMRhyeBOPB3wF3Ai8BzM9jvMWCZiCwSES/wZuDOo7bZA1wBICJn4IrDkZmZrtG41AfqWRRdRMwbQ6i+NMmF0YV8/OKPj5fbePLIk+U2SVMkhtJDbB/YzsHRg2Sdyu7UORNxeIlSylFKZZVS31ZKfRGYtlOcUioLvA/4DW4A+w6l1FYR+XguyA3wQeBdOZfV7cDVar70ENKUlKAVpD3SzvLa5TQHm6uuaF9zqJmPX/xxWkItfObRz7Bh/4Zym6QpEgpFb7KX7QPbORI/UrGdBo8bcxCR9wB/AywB8tu6Edz6SjMJShccHXPQzASl3N67/cn+ihmfeibEM3E++9hnea7vOa4++2pe3jXnbkaaKsEjHpqDzdT4a4p6noLVVhKRGFAL/AvwT3mrhpVSfSdl5UmgxUEzW9J2mr5kH4Opwaqoz5S203xh8xfYdGgTr1/2et6w/A26V/EpgN/00xRsKtoogkUbQ7pS0OKgmSuOchhKDdGX6qv4saptx+YbW77BvXvv5WULXzbvym1ojk/IE6I51FzwUflmKw7zr+++RnMcDDGo8ddQ468hnonTn+pnKDVUkaU4TMPk3ee+m4g3ws9f/DlD6SHee957sczqiqVoZs9odpQdgzuIeWNu/KxM37kWB80pSdAKErTc3POB1AD9yf6T7kRVaESEt57xVqLevHIbq67X4zyfIgymBxlKD1Hnr6Mx0FjyMiG6nao5pfEYHhoCDSyrXcbCyEKiVrTi0mH/ZMmf8J4V72Fr71Y++dAnGUoPldskTYnIz2zqSfSUtNy7FgeNJkfYG6Yj2sGymmU0+BsqaojSdR3r+OCqD7JneA83PXgTPYmecpukKSFZlR0fsnUwdfyKt4VEi4NGcxSWadEcamZ57XLaw+0EPeUreZDPBc0X8JGLPsJAaoCPPfgxuoers66UZu5knAzdI93sGNjBaGa0qOfS4qDRHAcRIeaLsSi2iCWxJdT56jDK/Jc5ve50brz4RhzlcNOGm3ih/4Wy2qMpDwk7wa6hXewZ2kMymyzKObQ4aDQzwO/x0xpuZXntclqCLfgMX9lsWRhdyM0X30zICvHJhz/Jk4d1uY1TleHMMDsGd7B/ZD8ZJ1PQY2tx0GhmgWmY1AfqWVq7lK5oF1FveQLYzaFmbr7kZlpDrXzmsc/w4L4HS26DpjJQKPpT/Wzv387h+GFsZ5pRlGaIFgeNZo6ErBAdkQ6W1S6jKdBU8gB2ja+Gj639GMtrl/Plx7/MXTvvKun5NZWFg8ORxBG2D2ynL9l30plNWhw0mpPEMiwag40srVmK3yztkJ9BK8iH13yYVc2ruG3rbdyx7Y6SpjtqKo+synJg9AAvDrx4UmnPlZOrp9FUOaZh0hntZNfgrpJ2qPOaXq674Dq+ueWb/OSFn7BrcBfnN5/PabWnsSCyQJfdOEVJOSn2Du8dH2hotmhx0GgKiGVYLIwuZOfgzpIW+TMNk2vOvYaGQAO/3f1bNh/eDLiur2U1yzit7jSW1y5nae1SfGb5guma0jM20NBs0YX3NJoikMgm2D20G1sVJjg4G5RSHIof4vn+59nWt41t/dvG+0SYYtIV7WJ53XJOqz2N0+pOo9ZfW3IbNaXn7IazdVVWjaYSiGfi7B7aXRGF/UbSI65Y9G/j+b7n2T6wfTz1sSnYxPLa5ZxWdxqn1Z5Ge6Rdu6LmIbMVB+1W0miKxNjodHuH96Io70NY2Bvm/ObzOb/5fACyTpadgzvHWxdberbwh31/ALQrSuOixUGjKSIRb4S2UBv7RveV25RJeAwPy2qXsax2Ga9c/MopXVFPbHsC0K6oUxUtDhpNkanx12Arm4Pxg+U25biICC2hFlpCLVzefjlwrCvq7t138+udvwa0K+pUoKjiICKvAL4AmMA3lVL/OsU2bwJuAhTwpFLqz4tpk0ZTDuoD9WSdLD3J6qmmOhtXVNATZFntsnGx0K6o6qdoAWkRMYHngZcC3cBjwFuUUs/kbbMMuAP4I6VUv4g0KaUOn+i4OiCtqWYOjBygL1W2IdgLyjGuqL5tdI9MZEWtalnFlQuv5Kz6s3SrogKopID0hcB2pdQOABH5AfAa4Jm8bd4FfEUp1Q8wnTBoNNVOa7iVrMrOiwF7TuSK2tKzhQe6H+CRA4/QEmzhioVXsK59HVFftMxWa2ZKMVsObwBeoZT669z7twFrlFLvy9vmp7iti0twXU83KaWOKRAjItcA1wB0dnZesHv37qLYrNGUAqUUe4b3MJIZKbcpRSVtp3n0wKP8bs/v2Na3DY/h4cKWC7mi8wrOrD8TkcoacW++U0kth5mefxmwHmgH7heRc5RSA/kbKaVuAW4B161UYhs1moIiInREOtg1tItENlFuc4qG1/RyafulXNp+KXuH93LP7nt4YN8DbNi/gbZQ23hrIuwNl9tUzRQU0xG4D+jIe9+eW5ZPN3CnUiqjlNqJ24pYVkSbNJqKwBCDzkhnWceFKCUdkQ6uPvtqvnrlV3nPivcQ8ob47jPf5T13v4cvP/5lnut7ThcMrDCK2XJ4DFgmIotwReHNwNGZSD8F3gJ8S0QagOXAjiLapNFUDB7D49ZhGtpZ8IFaKhWf6WNdxzrWdaxj99Du8dbEH/b9gfZIO1d0XsFlCy7TrYkKoKjlM0Tkj4HP48YTblVK/bOIfBzYqJS6U1yn478BrwBs4J+VUj840TF1tpJmvpGyU+wa3FXSQn2VRDKbZMP+Ddyz+x5eHHwRr+Flbdtarlx4JUtrlurYRIHQtZU0miqknIX6Komdgzu5e/fdPLjvQZJ2ks5IJ1cuvJJLF1xK0AqW27yqRouDRlOljGZG2T20u+x1mCqBRDbBg/se5O7dd7NraBc+08clbZdwxcIrWFKzpNzmVSVaHDSaKmYoPUT3cLcWiBxKKXYM7uDu3XezYf8GUnaKRbFFXNF5BZcsuISAJ1BuE6sGLQ4aTZXTn+xn/+j+cptRccQzcR7Y9wD37L6HPcN78Jt+Ll1wKVcsvIJFsUXlNq/i0eKg0cwDehI9HIofKrcZFYlSihcGXuDu3Xfz0P6HyDgZltQs4crOK1nbtha/p7TjeFcLWhw0mnnCwdGD9CZ7y21GRTOSHuGBfQ9w9+672Teyj4AnwGULLuPKhVfSGe0st3kVhRYHjWYesX9kP/2p/nKbUfEopXiu7znu2XMPjxx4hIyTYXntcta0rqEl1EJzsJmmYBNe01tuU8uGFgeNZh6hlKJ7uJuhTPUX6isVw+lh7u++n3t233NM7KbOX0dzsJnmUDPNweZx4WgONROyQmWyuDRUW20ljUZzAkSE9kg7u4d2M5odLbc5VUHEG+GVi1/JHy/6Y4YzwxwaPcSh+KFJr08cfoKB1MCk/cJWeFw0jhaPGl/NKdcZT4uDRlPhjBXq2z20m4Q9fwv1FRoRIeqNEvVGWVZ7bMm2ZDZ5jGgcih9i+8B2Htr/0KR0Yp/pc0Uj2ExTqGmScDQEGjANs5QfrSRocdBoqgDTMOmMdrJrcBcpJ1Vuc+YFfo+fhdGFLIwuPGZd1slyJH7EFY34IQ6OHuRw/DAHRg/wxJEnJtXCMsWkIdAwyUWV/1qtcQ4tDhpNlXAqFuorFx7DQ2u4ldZw6zHrHOXQn+yf1NoYE4/tA9sZzUx2/9X6amkINtAQaKDB30B9oN6dz00hK1SRListDhpNFWGZFgujC0/pQn3lxhCD+kA99YF6zqw/85j1I+kRDo4eHG91HBo9RE+ih50DO3ks+RhZZ/L35jN940KRLxz1/vrxZR6j9LdqLQ4aTZXhM33jMQgHp9zmaI4i7A2z1LuUpbVLj1nnKIeh9BC9iV56Ej3jU2+il95EL7uGdjGYGpy0jyDU+GrGhePolkd9oJ6IFSl460OLg0ZThQStIB2RDvYM79F1mKoIQwxqfDXU+GqOW0AwbafpS/ZNEo6x+T1De9h8aDNpJz1pH6/hPbblkTdf56+bta1aHDSaKiXsDbMgvIDuke5ym6IpIF7TS0uohZZQy5TrlVIMZ4anbH30JHp44vATBek4qcVBo6liYr4YtrI5MHqg3KZoSkR+iu7xCg5m7Mx466M36YrG0zw9q/NocdBoqpw6fx22Y3M4cbjcpmgqBMu03FTaUPP4so/xsVkdwyi0URqNpvQ0Bhup99eX2wzNPKKo4iAirxCRbSKyXUT+6QTbvV5ElIjMuO6HRqOZTEuohRpfTbnN0MwTiuZWEhET+ArwUqAbeExE7lRKPXPUdhHgA8AjxbJFozlVaAu1EfFGyNgZMk7eZGd0vwjNrChmzOFCYLtSageAiPwAeA3wzFHbfQL4NPAPRbRFozklGAtWToVSapJYZJwMWSc7SURsZZfYYk2lUkxxWADszXvfDazJ30BEzgc6lFK/FJHjioOIXANcA9DZqQfw0GjmgojgNb1urR9r6m1sx56yxZHfEtH9Kk4NypatJCIG8O/A1dNtq5S6BbgF3PEcimuZRnPqYhompmHi5/hDbea3PI5pfWj31byhmOKwD+jIe9+eWzZGBDgbuDfX7bsFuFNEXq2U0qP5aDQVimVYWMZxmh647qukneTA6AESWV1ivFopZrbSY8AyEVkkIl7gzcCdYyuVUoNKqQalVJdSqgt4GNDCoNFUOSJCwBNgUXQRjYHGcpujmSNFEwelVBZ4H/Ab4FngDqXUVhH5uIi8uljn1Wg0lYGI0BRsYlF00QlbGprKpKgxB6XUr4BfHbVsym56Sqn1xbRFo9GUh6AVZElsCQdGDzCYHpx+B01FoHtIazSaomMaJu2RdtrD7Zgy/4bUnI9ocdBoNCUj5ouxJLaEoCdYblM006DFQaPRlBTLtFgUW0RzsBmh8obH1LhocdBoNGWhIdDAotgifIav3KZopkCLg0ajKRsBT4DFNYup881+pDJNcdHioNFoyoohBq3hVjoiHXhEDzFTKWhx0Gg0FUHUG2VxzWIiVqTcpmjQ4qDRaCoIy7DojHbSEmzB0LensqKvvkajqTjqA/UsrlmM3zx+AUBNcdHioNFoKhKf6WNxbLEe/rRMaHHQaDQVi4jQEmqhK9qlg9UlRouDRqOpeEJWiCU1S447yp2m8Ghx0Gg0VYHH8NAR6WBBaIEOVpcAfYU1Gk1VUeOvYUnNEgKeQLlNmddocdBoNFWH1/SODyak6zMVBy0OGo2mKhkbTKgr2oXX8JbbnHmHFgeNRlPVBK0gi2OLqfHVlNuUeYUWB41GU/WYhsmC8AI9mNAUGBhErdlneenEYY1GM2+I+WIEPUH2jexjNDtabnPKhkc8hL1hIt4IYSuMIbNvBxS15SAirxCRbSKyXUT+aYr114vIMyLylIjcIyILi2mPRqOZ/1imRVesi+Zg8ynVirAMi3p/PV3RLpbXLmdBeAFRb3ROwgBFbDmIiAl8BXgp0A08JiJ3KqWeydvscWCVUiouIu8BPgP8WbFs0mg0pw4NgQYaAg0ksgnimbg7ZeNkVbbcphUMv+kn4o0Q8UYKntpbTLfShcB2pdQOABH5AfAaYFwclFK/z9v+YeAvimiPRqM5BQl4AgQ8AeoDbo2mZDZJPBtnNDNKPFN9YhH0BIl4I0S9Ubxm8bK0iikOC4C9ee+7gTUn2P6dwK+nWiEi1wDXAHR2dhbKPo1Gcwri9/jxe/zU+d3R51J2ingmJxbZOBknU2YLJyMIYSs83kLwGKUJFVdEQFpE/gJYBaybar1S6hbgFoBVq1apEpqm0WjmOT7Th8/0UeuvBSBtp12xyLoti7STLrlNppjjghC2wphG6WMnxRSHfUBH3vv23LJJiMiVwEeAdUqpVBHt0Wg0mmnxml68ppcaagDI2JlJbqiUU5zblEc8RL1RIt4IISuESHl7fhdTHB4DlonIIlxReDPw5/kbiMhK4OvAK5RSh4toi0aj0cwJy7SImTFivhgAGSczKcCdtJNzPrbP8I27i4JWsFAmF4SiiYNSKisi7wN+A5jArUqprSLycWCjUupO4LNAGPifnEruUUq9ulg2aTQazcliGRYx34RYZJ0s8Wx8XDASduKE+wc8gfEWgs/0lcLkOSFKVZcLf9WqVWrjxo3lNkOj0WimxHbsCTdUNk4qmyJouRlGESuCZVplsUtENimlVs10+4oISGs0Gs18wTTMcVdRNaNrK2k0Go3mGLQ4aDQajeYYtDhoNBqN5hi0OGg0Go3mGLQ4aDQajeYYtDhoNBqN5hi0OGg0Go3mGLQ4aDQajeYYtDhoNBqN5hiqrnyGiAwD28ptB9AA9GgbgMqwoxJsgMqwoxJsgMqwoxJsgMqw4zSl1Iy7bVdj+Yxts6kPUixEZGO57agEGyrFjkqwoVLsqAQbKsWOSrChUuwQkVkVpdNuJY1Go9EcgxYHjUaj0RxDNYrDLeU2IEcl2FEJNkBl2FEJNkBl2FEJNkBl2FEJNkBl2DErG6ouIK3RaDSa4lONLQeNRqPRFBktDhqNRqM5Bi0OGo1GozmGqhEHEakTkbpy26GpTETk/HLboKk8RKSh3DZUKxUtDiLSKSI/EJEjwCPAoyJyOLesq8zmnXKIyOki8msR+aWILBGR20RkQEQeFZEzSmjH+UdNFwB3isjKU10kRCQqIheISG25bSk1InKViOwUkT/kfgtbgUdEpFtEriiDPbUiEi31eQuGUqpiJ+Ah4M8AM2+ZCbwZeLgC7NtSwnN1AD8AHgBuAKy8dT8tkQ33A38CvAXYnfseJLfsnhJeCwfYAPw+b0rkXv+vRDb8Vd58O3APMJCza3kJr8X3gIbc/MuBPcDdue/njSWyoQ/4JnAFuQzIckzAE8AZwFqgF7got/wMYHOJbGgDvgMMAnbu+9gD3JT/ny3hNWkGzs9NzbPat1xf5Aw/2AtzWVdgG153nOn1wJESXovfAdcC5wFfyt2E6nPrHi+RDY/nzW8/al1J/ny5c70euA+4Km/ZzlKd/+jPC9wBXIPbEn9tiYVyS978BqArN98APFkiG7YB7wMeBPYBXxi7MZfxO9l71LonSmTD/wHrc/OvA/4DCAGfBG4p4bU4D3gYeDb3sHA38Fxu2fkzOUal11baJCJfBb4N7M0t6wDeATxeIht+CHwfmKpDiL9ENgA0KqW+lpt/v4j8BXC/iLyaqW0rBmbe/L8ftc5bIhtQSv1YRH4DfEJE/gr4IKW7BlOxXCn1ptz8/4rIx0p4bkNEokqpIdwW1R4ApVSPiJTq/z2qlPoy8GUR6cRtUX5VRGqAHyilbiiRHQMi8m4gCvSLyN/hCveVwEiJbKhXSt0LoJT6iYh8RCk1CnxURJ4rkQ0AtwHvVko9kr9QRC4CvgWsmO4AlS4ObwfeCdwMLMgt6wZ+DvxXiWx4CvicUurpo1eIyJUlsgHAEhG/UioJoJT6nogcBH6D+2RSCr4iImGl1IhS6qtjC0VkKe6TSclQSo0AfyciK3EfHsKlPD/QLiJfxHWrNYqIpZTK5NZZJbTjZuD3IvIV3Cf3/xGRO4GXAHeVyAYZm1FK7QE+A3xGRE7HdQuXincAH8UVyZfhuj9/g+tie1eJbDiSe3D7PW7LYReAiAiljfGGjhYGAKXUwyIyo/uF7iE9DSJyGbA796M/et0qpdSsKh2ehB1/h9tsvu+o5SuBzyilXloKOyqR3B8vknt6LtU533HUojuVUv0i0gL8bQmflsfE+V3ActwHvm7cONRvSnT+f1dKXV+Kc1U6uZbT54AzcWMg/6CUOiAi9bjuph+XyI4vAktw4x/5Xpe347pg3zftMapVHETkVUqpX5TbDo1LpXwflWKHprI4FX8XInIV8BomvC77cB9ifjWT/Ss6lXUaVpfbABF5VbltgIqxo+zfR46y21Eh30dF2FEJNuQ45X4XSqlfK6WuVUr9SW66dqbCAJUfcyDnt5xK/W4sn1XjrAYq4WmkZHZUyvdRKXYch1Pud1EpNujfxfSIyDVKqWkrtFa0W0lEPoQbVPoBrh8V3JzyN+NmQfxriew43g/u2VKcv1LsqKDvo1Ls0L+LyrJB/y5mgIi8Wyn19Wm3q3BxeB44Ky8LZGy5F9iqlFpWAhsq5QdXdjsq4fuoFDsq4fuoFDsqwYacHfp3MQNE5C+VUt+adsNSdcqYY0eO54CFUyxfiDuWdClseJ4pejbi5vWXpCNepdhRCd9HpdhRCd9HpdhRCTbo38WsbNwzk+0qPeZwHXCPiLzARDpWJ7AUt0dmKXBwu8TvPmp5a25dqagEO66j/N9HpdhRCd9HpdhRCTaA/l2MIyJPHW8VbkmNaalocVBK3SUiy4ELmey/e0wpZZfIjOso/w+uIuyokO+jUuy4Dv27qCQb9O9iMs24tbb6j1ouuGVWpqWiYw6VgogYlPmGWEl2aFwq5fuoBDsqwYZKoRKuhYj8F/AtpdQfplj330qpP5/2GFocNBqNRnM01dwJTqPRaDRFQouDRqPRaI5Bi4Om4hF3xLk3lNuOSkJErhORYLnt0MxftDhoNCWiwOMrXAdocdAUDS0OmopCRN4uIk+JyJMi8t28VZeLyAYR2THWihCRsIjcIyKbRWSLiLwmt7xLRJ4VkW+IyFYR+a2IBHLrVueO/4SIfFZEns4tN3PvH8utf/cUtnWJyHMi8v3c8X809vQu7rjN94nIJhH5jYi05pbfKyKfF5GNwAdy59+Q+3yPikjkeOcWkfW5/X+Ud14Rkb/FzaX/vYj8Prftf4rIxtznvTnP5j/O7btJRL4oIr/ILQ+JyK05Gx4fu3YazTjl7q2nJz2NTcBZuD1Mx8ZErsu93gb8D+7DzJnkhijF7acTzc03ANtx87i7gCxwXm7dHcBf5OafBtbm5v8VeDo3fw3w0dy8D9gILDrKvi7cEecuyb2/Ffh73MF9NuCO1gfuADe35ubvBb6am/cCO4DVuffR3GeY8tzAetyxiNtzn/0h4NLcdrvGrtNR18rMnfNc3JEK9459DuB24Be5+U/lXZOa3HUPlfs3oKfKmSq6E5zmlOOPgP9RSvUAKKX68tb9VCnlAM+IyFgPTwE+JSKX4/Y+XcBE78+dSqkncvObgC5xh62MKKUeyi3/b2CsjPLLgHPzYhsxYBmw8ygb9yqlHszNfw/4W9wR184Gfici4N6gD+Tt88Pc62nAAaXUY7nPNwQgIsc7dxp4VCnVndvuCVyBOiZ3HXiTiFyDKzatuCJqADuUUmOf4XZcIRr7vK8Wkb/PvffjdtaqiOJwmvKjxUFTLaTy5seGpXwr0AhcoJTKiMguJsb1zt/eBgLTHF+A96vpR087umOQyu27VSm19jj7jM7l3CKynmM/xzH/WRFZhNuCWa3c0ehuY/rxzQV4vVJq2zTbaU5RdMxBU0n8H/BGcYdURETqptk+BhzOCcNLcAusHRel1AAwLCJrcovenLf6N8B7RMTKnXu5TD3WbqeIjInAn+M+xW/DHUd6bW5fS0TOmmLfbUCriKzObRfJBalneu58hoFIbj6KK0CDuVbVVXnnWywiXbn3+eM5/wZ4v+SaOuION6vRjKNbDpqKQSm1VUT+GbhPRGzgceDqE+zyfeDnIrIF10//3AxO807gGyLiAPfh+vQBvonrstmcu2EeAf50iv23Ae8VkVuBZ4D/VEqlcy6hL4pIDPd/9Xlg61GfLy0ifwZ8KRcgTwBXzuLc+dwC3CUi+5VSLxGRx3Offy/wYO58CRH5m9x2o8Bjeft/ImfjU+KWe9jJhItNo9HlMzSnFiISVkqN5Ob/CWhVSn1ghvt24QZ0zy6iiQVl7PPmROcruGWj/6PcdmkqH+1W0pxqvDKXxvo0cBnwyXIbVGTelQtkb8V1w007AphGA7rloNFoNJop0C0HjUaj0RyDFgeNRqPRHIMWB41Go9EcgxYHjUaj0RyDFgeNRqPRHMP/B7jRXEbG0VP6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import os\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    print(results, name)\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def getMeanSTD(inputarray):\n",
    "    mean = []\n",
    "    std = []\n",
    "    for i in range(len(inputarray)):\n",
    "        mean.append(np.mean(inputarray[i]))\n",
    "        std.append(np.std(inputarray[i]))\n",
    "    return np.array(mean), np.array(std)\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for t_name in [\"narrow\", \"turtle\", \"wide\"]:\n",
    "        name = p_name + \"_\" + t_name\n",
    "        print(name, p_dict)\n",
    "        mean, std = getMeanSTD(p_dict[name])\n",
    "        plt.plot(np.array(ch_perc_array), mean)\n",
    "        plt.fill_between(np.array(ch_perc_array), mean - std, mean + std, alpha=0.2)\n",
    "        names.append(name.split(\"_\")[1])\n",
    "    plt.legend(names, loc='upper left')\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    if not os.path.exists('graphs'):\n",
    "        os.makedirs('graphs')\n",
    "\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\", bbox_inches='tight')\n",
    "\n",
    "for n in lambdas[p_name]:\n",
    "    if n == \"has_sol\":\n",
    "        continue\n",
    "    plt_dict(get_data(result, n), n, '{}_{}'.format(p_name, n))\n",
    "plt_dict(get_data(result, 'hdiversity'), 'Hamming Diversity', '{}_{}'.format(p_name,'hammingdiversity'))\n",
    "# plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "# plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "# plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing solid probability with 0.5 change rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error: the file binary_narrow_noaction_heatmap_changes_100M could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6908\\2425268192.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[0mregions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[0mboth_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0msolid_perc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Testing {} at solid percentage of {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msolid_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file binary_narrow_noaction_heatmap_changes_100M could not be found"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from stable_baselines.common.policies import MlpPolicy, CnnPolicy, FeedForwardPolicy\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n",
    "from stable_baselines.results_plotter import load_results, ts2xy\n",
    "from stable_baselines import PPO2\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import pdb\n",
    "\n",
    "p_name = \"sokoban\"\n",
    "rep_names = [\"narrow\", \"turtle\"]\n",
    "exp_names = [\"noaction_heatmap_changes_100M\", \"heatmap_changes_100M\"]\n",
    "\n",
    "def get_env_func(p_name, r_name, solid_perc=0.2):\n",
    "    def get_env():\n",
    "        env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), 28, random_tile=False, change_percentage=0.4, probs={\"empty\":1-solid_perc, \"solid\":solid_perc})\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "pathlength_perc = {}\n",
    "pathlength={}\n",
    "regions_perc = {}\n",
    "regions={}\n",
    "both_perc = {}\n",
    "for i in range(len(exp_names)):\n",
    "    r_name = rep_names[i]\n",
    "    e_name = exp_names[i]\n",
    "    m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "    pathlength_perc[m_name] = []\n",
    "    pathlength[m_name] = []\n",
    "    regions_perc[m_name] = []\n",
    "    regions[m_name] = []\n",
    "    both_perc[m_name] = []\n",
    "    model = PPO2.load(m_name)\n",
    "    for solid_perc in np.arange(0.0, 1.01, 0.05):\n",
    "        print(\"Testing {} at solid percentage of {}\".format(m_name,solid_perc))\n",
    "        temp_path = []\n",
    "        temp_length = []\n",
    "        temp_regions = []\n",
    "        temp_number = []\n",
    "        temp_both = []\n",
    "        for i in range(20):\n",
    "            env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "            done = False\n",
    "            obs = env.reset()\n",
    "            while not done:\n",
    "                action = get_action(env, model, 0)\n",
    "                obs, rewards, done, info = env.step(action)\n",
    "            temp_path.append([0,1][info[0]['path-length']>=48])\n",
    "            temp_length.append(info[0]['path-length'])\n",
    "            temp_regions.append([0,1][info[0]['regions']==1])\n",
    "            temp_number.append(info[0]['regions'])\n",
    "            temp_both.append([0,1][temp_path[-1] == 1 and temp_regions[-1] == 1])\n",
    "        pathlength_perc[m_name].append(np.mean(temp_path))\n",
    "        pathlength[m_name].append(np.mean(temp_length))\n",
    "        regions_perc[m_name].append(np.mean(temp_regions))\n",
    "        regions[m_name].append(np.mean(temp_number))\n",
    "        both_perc[m_name].append(np.mean(temp_both))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (21,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\2121413545.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregions_perc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mregions_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2840\u001b[0m     return gca().plot(\n\u001b[0;32m   2841\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2842\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2844\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1741\u001b[0m         \"\"\"\n\u001b[0;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1743\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1744\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1745\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[0;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    401\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (21,) and (0,)"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Region == 1')\n",
    "plt.savefig('const_regions.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path >= 48')\n",
    "plt.savefig('const_path.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path Length')\n",
    "plt.savefig('pathlength.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Number of Regions')\n",
    "plt.savefig('regions.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = wrappers.CroppedImagePCGRLWrapper(\"zelda-turtle-v0\", 22, random_tile=False, change_percentage=0.2)\n",
    "env.reset().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2280549686.py, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_3900\\2280549686.py\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\u001b[0m\n\u001b[1;37m                                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def get_tpkldiv_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "diversity={\n",
    "    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\n",
    "    'tpkldiv': lambda lvl1, lvl2: return calc_tp_kldiv(lvl1, lvl2, 3)\n",
    "    ''\n",
    "}\n",
    "\n",
    "def get_diversity(lvls, d_type):\n",
    "    result = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                if d_type == \"hamming\":\n",
    "                    \n",
    "        result.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return result\n",
    "\n",
    "def get_hamming_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "env_func = get_env_func(\"sokoban\", \"wide\", 1, 10)\n",
    "lvls = []\n",
    "for i in range(5):\n",
    "    env = DummyVecEnv([env_func])\n",
    "    done = False\n",
    "    obs = env.reset()\n",
    "    while not done:\n",
    "        action, _ = model.predict(obs)\n",
    "        obs, rewards, done, info = env.step(action)\n",
    "    lvls.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "diversity = get_hamming_diversity(lvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lvls' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\3401017067.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcomp_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mcomp_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mcomp_both_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'lvls' is not defined"
     ]
    }
   ],
   "source": [
    "import zlib\n",
    "\n",
    "\n",
    "comp_1 = zlib.compress(lvls[0].tobytes(), level=9)\n",
    "comp_2 = zlib.compress(lvls[1].tobytes(), level=9)\n",
    "comp_both_1 = zlib.compress(lvls[0].tobytes() + lvls[1].tobytes(), level=9)\n",
    "result_1 = (len(comp_both_1) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "comp_both_2 = zlib.compress(lvls[1].tobytes() + lvls[0].tobytes(), level=9)\n",
    "result_2 = (len(comp_both_2) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "print((result_1 + result_2)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_narrow_noaction_heatmap_changes_100M\n",
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      "  [1.]]], (28, 28, 1), float32)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\138669907.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tests/{}.{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me_format\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found"
     ]
    }
   ],
   "source": [
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=True, add_changes=True, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=True, add_changes=True, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "print(m_name)\n",
    "print(p_name)\n",
    "print(r_name)\n",
    "print(get_env_func(p_name, r_name, ch_perc)().observation_space)\n",
    "model = PPO2.load('tests/{}.{}'.format(m_name, e_format))\n",
    "env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "obs = env.reset()\n",
    "print(env.observation_space)\n",
    "action = get_action(env, model, 0)\n",
    "obs, rewards, done, info = env.step(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  [0. 0. 0. 0. 0.]]], [[[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]], (5, 5, 5), float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_env_func(p_name, r_name)().observation_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sokoban_narrow': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'crate': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'target': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'sollength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'ratio_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'sokoban_turtle': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'crate': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'target': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'sollength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'ratio_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'sokoban_wide': {'player': [[1.2],\n",
       "   [1.2],\n",
       "   [0.8],\n",
       "   [0.675],\n",
       "   [0.675],\n",
       "   [0.475],\n",
       "   [0.55],\n",
       "   [0.525],\n",
       "   [0.525],\n",
       "   [0.425],\n",
       "   [0.425]],\n",
       "  'crate': [[1.175],\n",
       "   [1.25],\n",
       "   [0.95],\n",
       "   [0.9],\n",
       "   [0.825],\n",
       "   [0.675],\n",
       "   [0.3],\n",
       "   [0.4],\n",
       "   [0.275],\n",
       "   [0.375],\n",
       "   [0.375]],\n",
       "  'target': [[1.2],\n",
       "   [1.625],\n",
       "   [1.175],\n",
       "   [1.05],\n",
       "   [0.875],\n",
       "   [0.8],\n",
       "   [0.4],\n",
       "   [0.5],\n",
       "   [0.275],\n",
       "   [0.4],\n",
       "   [0.35]],\n",
       "  'regions': [[1.95],\n",
       "   [1.425],\n",
       "   [1.0],\n",
       "   [1.025],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0]],\n",
       "  'sollength': [[0.275],\n",
       "   [0.15],\n",
       "   [0.925],\n",
       "   [1.725],\n",
       "   [0.775],\n",
       "   [2.525],\n",
       "   [0.85],\n",
       "   [0.725],\n",
       "   [1.275],\n",
       "   [0.625],\n",
       "   [0.525]],\n",
       "  'iterations': [[1.0],\n",
       "   [2.175],\n",
       "   [6.475],\n",
       "   [19.725],\n",
       "   [54.25],\n",
       "   [110.6],\n",
       "   [214.15],\n",
       "   [202.95],\n",
       "   [414.7],\n",
       "   [442.8],\n",
       "   [549.075]],\n",
       "  'changes': [[1.0],\n",
       "   [2.0],\n",
       "   [5.0],\n",
       "   [6.825],\n",
       "   [9.85],\n",
       "   [11.225],\n",
       "   [13.95],\n",
       "   [15.65],\n",
       "   [15.55],\n",
       "   [17.225],\n",
       "   [18.2]],\n",
       "  'player_const': [[0.5],\n",
       "   [0.375],\n",
       "   [0.7],\n",
       "   [0.675],\n",
       "   [0.575],\n",
       "   [0.475],\n",
       "   [0.5],\n",
       "   [0.475],\n",
       "   [0.525],\n",
       "   [0.425],\n",
       "   [0.425]],\n",
       "  'ratio_const': [[0.175],\n",
       "   [0.2],\n",
       "   [0.45],\n",
       "   [0.525],\n",
       "   [0.475],\n",
       "   [0.3],\n",
       "   [0.2],\n",
       "   [0.3],\n",
       "   [0.225],\n",
       "   [0.225],\n",
       "   [0.275]],\n",
       "  'has_sol': [[0.025],\n",
       "   [0.025],\n",
       "   [0.125],\n",
       "   [0.125],\n",
       "   [0.075],\n",
       "   [0.15],\n",
       "   [0.125],\n",
       "   [0.05],\n",
       "   [0.075],\n",
       "   [0.075],\n",
       "   [0.1]],\n",
       "  'success': [[0.0],\n",
       "   [0.0],\n",
       "   [0.0],\n",
       "   [0.05],\n",
       "   [0.0],\n",
       "   [0.075],\n",
       "   [0.025],\n",
       "   [0.025],\n",
       "   [0.05],\n",
       "   [0.025],\n",
       "   [0.0]],\n",
       "  'hdiversity': [[0.404],\n",
       "   [0.43499999999999994],\n",
       "   [0.354],\n",
       "   [0.309],\n",
       "   [0.23199999999999998],\n",
       "   [0.146],\n",
       "   [0.082],\n",
       "   [0.08],\n",
       "   [0.06899999999999999],\n",
       "   [0.05700000000000001],\n",
       "   [0.049]]}}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_start = [\n",
    "       [0, 4, 0, 1, 0],\n",
    "       [2, 1, 1, 1, 1],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "narrow_temp = [\n",
    "       [0, 4, 0, 4, 0],\n",
    "       [2, 3, 1, 1, 1],\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 3, 1, 0, 0],\n",
    "       [1, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_temp = [\n",
    "       [0, 4, 0, 0, 0],\n",
    "       [0, 1, 3, 1, 0],\n",
    "       [0, 0, 4, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "wide_temp = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [4, 1, 1, 2, 1],\n",
    "       [0, 0, 0, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "second_start = [\n",
    "       [1, 0, 0, 3, 3],\n",
    "       [0, 0, 0, 1, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "narrow_last = [\n",
    "       [1, 0, 0, 0, 0],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [0, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [4, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_last = [\n",
    "       [1, 0, 0, 3, 4],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 4, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 0, 4, 1, 1],\n",
    "       [1, 0, 0, 0, 2],\n",
    "       [1, 3, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "third_start = [\n",
    "       [4, 0, 1, 0, 0],\n",
    "       [1, 0, 2, 2, 0],\n",
    "       [1, 1, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 1, 0, 0, 1]]\n",
    "\n",
    "narrow_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 0, 2, 0],\n",
    "       [1, 3, 0, 4, 0],\n",
    "       [0, 3, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 1]]\n",
    "\n",
    "turtle_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 3, 0],\n",
    "       [0, 3, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 0, 0],\n",
    "       [1, 1, 0, 3, 0],\n",
    "       [0, 1, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info[0]['terminal_observation'].argmax(axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.525"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(result['sokoban_wide']['sollength'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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