From 5214c6836e359887ea9638320c6290dca0bddbff Mon Sep 17 00:00:00 2001 From: Raphael Boige Date: Sat, 22 Apr 2023 19:49:06 +0200 Subject: [PATCH 01/55] feat: add sudoku --- README.md | 2 + docs/api/environments/sudoku.md | 8 + docs/env_anim/sudoku.gif | Bin 0 -> 39583 bytes docs/env_img/sudoku.png | Bin 0 -> 41100 bytes docs/environments/sudoku.md | 35 +++ examples/training.ipynb | 287 ++++++++++++++++-- jumanji/__init__.py | 2 + jumanji/environments/__init__.py | 1 + jumanji/environments/logic/sudoku/__init__.py | 16 + jumanji/environments/logic/sudoku/conftest.py | 27 ++ .../environments/logic/sudoku/constants.py | 28 ++ jumanji/environments/logic/sudoku/env.py | 204 +++++++++++++ jumanji/environments/logic/sudoku/env_test.py | 93 ++++++ .../environments/logic/sudoku/generator.py | 84 +++++ jumanji/environments/logic/sudoku/types.py | 45 +++ jumanji/environments/logic/sudoku/utils.py | 123 ++++++++ jumanji/environments/logic/sudoku/viewer.py | 167 ++++++++++ jumanji/training/configs/config.yaml | 2 +- jumanji/training/networks/__init__.py | 4 + jumanji/training/networks/sudoku/__init__.py | 13 + .../training/networks/sudoku/actor_critic.py | 95 ++++++ jumanji/training/networks/sudoku/random.py | 62 ++++ jumanji/training/setup_train.py | 14 + 23 files changed, 1280 insertions(+), 32 deletions(-) create mode 100644 docs/api/environments/sudoku.md create mode 100644 docs/env_anim/sudoku.gif create mode 100644 docs/env_img/sudoku.png create mode 100644 docs/environments/sudoku.md create mode 100644 jumanji/environments/logic/sudoku/__init__.py create mode 100644 jumanji/environments/logic/sudoku/conftest.py create mode 100644 jumanji/environments/logic/sudoku/constants.py create mode 100644 jumanji/environments/logic/sudoku/env.py create mode 100644 jumanji/environments/logic/sudoku/env_test.py create mode 100644 jumanji/environments/logic/sudoku/generator.py create mode 100644 jumanji/environments/logic/sudoku/types.py create mode 100644 jumanji/environments/logic/sudoku/utils.py create mode 100644 jumanji/environments/logic/sudoku/viewer.py create mode 100644 jumanji/training/networks/sudoku/__init__.py create mode 100644 jumanji/training/networks/sudoku/actor_critic.py create mode 100644 jumanji/training/networks/sudoku/random.py diff --git a/README.md b/README.md index fca9add9a..561e8592e 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,7 @@ RubiksCube Game2048 Minesweeper + Sudoku

@@ -81,6 +82,7 @@ problems. | πŸ”’ Game2048 | Logic | `Game2048-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/game_2048/) | [doc](https://instadeepai.github.io/jumanji/environments/game_2048/) | | πŸ’£ Minesweeper | Logic | `Minesweeper-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/minesweeper/) | [doc](https://instadeepai.github.io/jumanji/environments/minesweeper/) | | 🎲 RubiksCube | Logic | `RubiksCube-v0`
`RubiksCube-partly-scrambled-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/rubiks_cube/) | [doc](https://instadeepai.github.io/jumanji/environments/rubiks_cube/) | +| ✏️ Sudoku | Logic | `Sudoku-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/sudoku/) | [doc](https://instadeepai.github.io/jumanji/environments/sudoku/) | | πŸ“¦ BinPack (3D BinPacking Problem) | Packing | `BinPack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/bin_pack/) | [doc](https://instadeepai.github.io/jumanji/environments/bin_pack/) | | 🏭 JobShop (Job Shop Scheduling Problem) | Packing | `JobShop-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/job_shop/) | [doc](https://instadeepai.github.io/jumanji/environments/job_shop/) | | πŸŽ’ Knapsack | Packing | `Knapsack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/knapsack/) | [doc](https://instadeepai.github.io/jumanji/environments/knapsack/) | diff --git a/docs/api/environments/sudoku.md b/docs/api/environments/sudoku.md new file mode 100644 index 000000000..489e363cd --- /dev/null +++ b/docs/api/environments/sudoku.md @@ -0,0 +1,8 @@ +::: jumanji.environments.logic.sudoku.env.Sudoku + selection: + members: + - __init__ + - reset + - step + - observation_spec + - action_spec diff --git a/docs/env_anim/sudoku.gif b/docs/env_anim/sudoku.gif new file mode 100644 index 0000000000000000000000000000000000000000..b47a7689f2b67a7a2608675e72cfa288a9555745 GIT binary patch literal 39583 zcmeFZ)nC-#8}D)6>`2 zH!v_TG&D3aGBP$cHZd_VH8nLeGcz|gx3IAI{Q0w`rKPpCwT+F9t*xz{ot?eCy@P{; zqobpflasTv^OrAQTwGjSU0vPW+`fMO>hA9D;oiYWn*RNl{fB(L@x%u)heRUjk=RO78tM{o6)Eo5T;Tr+g8l)5$_;=3j{j-ne=-UF z-{${`uUr%7*!4QGci}p2?Suf2BWKUou;y 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+ +We provide here a Jax JIT-able implementation of the +[Sudoku](https://en.wikipedia.org/wiki/Sudoku) puzzle game. + + +## Observation +The observation given to the agent consists of: + + +- `board`: jax array (int32) of shape (9,9): + empty cells are represented by -1, and filled cells are represented by 0-8. +- `action_mask`: jax array (bool) of shape (9,9,9): + indicates which actions are valid. + + + +## Action +The action space is a `MultiDiscreteArray` of integer values representing coordinates of the square +to explore and the digits to write in the cell, e.g. `[3, 6, 8]` for writing the digit `9` +the cell located on the third row and sixth column. + + +## Reward +The reward is `1` at the end of the episode if the board is correctly solved, and `0` in every +other case. + + +## Registered Versions πŸ“– +- `Sudoku-v0`, the classic [game](https://en.wikipedia.org/wiki/Sudoku) on +a 9x9 grid. diff --git a/examples/training.ipynb b/examples/training.ipynb index 4ac1e146c..18d39ae83 100644 --- a/examples/training.ipynb +++ b/examples/training.ipynb @@ -4,89 +4,314 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } + "tags": [] + }, + "outputs": [], + "source": [ + "# pip install -e ../" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" + "env: JAX_PLATFORMS=cpu\n" ] } ], "source": [ - "%pip install --quiet -U pip -r ../requirements/requirements-training.txt ../." + "%env JAX_PLATFORMS=cpu" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false - } + }, + "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:jax._src.lib.xla_bridge:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - } - ], + "outputs": [], "source": [ "import warnings\n", + "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "from jumanji.training.train import train\n", - "from hydra import compose, initialize" + "from hydra import compose, initialize\n", + "\n", + "from jumanji.training.train import train" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false - } + }, + "tags": [] }, "outputs": [], "source": [ - "env = \"maze\" # @param ['bin_pack', 'cleaner', 'connector', 'cvrp', 'game_2048', 'job_shop', 'knapsack', 'maze', 'minesweeper', 'rubiks_cube', 'snake', 'tsp']\n", - "agent = \"random\" # @param ['random', 'a2c']" + "env = \"sudoku\" # @param ['bin_pack', 'cleaner', 'connector', 'cvrp', 'game_2048', 'job_shop', 'knapsack', 'maze', 'minesweeper', 'rubiks_cube', 'snake', \"sudoku\", 'tsp']\n", + "agent = \"a2c\" # @param ['random', 'a2c']" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 28, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false - } + }, + "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'agent': 'a2c', 'seed': 0, 'logger': {'type': 'terminal', 'save_checkpoint': True, 'name': '${agent}_${env.name}'}, 'env': {'name': 'sudoku', 'registered_version': 'Sudoku-v0', 'network': {'num_channels': 32, 'policy_layers': [64, 64], 'value_layers': [128, 128]}, 'training': {'num_epochs': 100, 'num_learner_steps_per_epoch': 500, 'n_steps': 20, 'total_batch_size': 128}, 'evaluation': {'eval_total_batch_size': 200, 'greedy_eval_total_batch_size': 200}, 'a2c': {'normalize_advantage': False, 'discount_factor': 0.997, 'bootstrapping_factor': 0.95, 'l_pg': 1.0, 'l_td': 1.0, 'l_en': 0.01, 'learning_rate': 0.0004}}}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "with initialize(version_base=None, config_path=\"../jumanji/training/configs\"):\n", - " cfg = compose(config_name=\"config.yaml\", overrides=[f\"env={env}\", f\"agent={agent}\", \"logger.type=terminal\", \"logger.save_checkpoint=true\"])\n", + " cfg = compose(\n", + " config_name=\"config.yaml\",\n", + " overrides=[\n", + " f\"env={env}\",\n", + " f\"agent={agent}\",\n", + " \"logger.type=terminal\",\n", + " \"logger.save_checkpoint=true\",\n", + " ],\n", + " )\n", "cfg" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:agent: a2c\n", + "seed: 0\n", + "logger:\n", + " type: terminal\n", + " save_checkpoint: true\n", + " name: ${agent}_${env.name}\n", + "env:\n", + " name: sudoku\n", + " registered_version: Sudoku-v0\n", + " network:\n", + " num_channels: 32\n", + " policy_layers:\n", + " - 64\n", + " - 64\n", + " value_layers:\n", + " - 128\n", + " - 128\n", + " training:\n", + " num_epochs: 100\n", + " num_learner_steps_per_epoch: 500\n", + " n_steps: 20\n", + " total_batch_size: 128\n", + " evaluation:\n", + " eval_total_batch_size: 200\n", + " greedy_eval_total_batch_size: 200\n", + " a2c:\n", + " normalize_advantage: false\n", + " discount_factor: 0.997\n", + " bootstrapping_factor: 0.95\n", + " l_pg: 1.0\n", + " l_td: 1.0\n", + " l_en: 0.01\n", + " learning_rate: 0.0004\n", + "\n", + "INFO:root:{'devices': [CpuDevice(id=0)]}\n", + "INFO:root:Experiment: a2c_sudoku.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "board (1, 9, 9)\n", + "embedding (1, 800)\n", + "(1, 9, 9, 9)\n", + "board (1, 9, 9)\n", + "embedding (1, 800)\n", + "(1, 9, 9, 9)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Starting logger.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "board (1, 9, 9)\n", + "embedding (1, 800)\n", + "(1, 9, 9, 9)\n", + "Tracedwith\n", + "Tracedwith\n", + "Tracedwith Tracedwith Tracedwith\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Eval Stochastic >> Env Steps: 0.00e+00 | Episode Length: 31.815 | Episode Return: 0.000 | Time: 1.501\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "board (1, 9, 9)\n", + "embedding (1, 800)\n", + "(1, 9, 9, 9)\n", + "Tracedwith\n", + "Tracedwith\n", + "Tracedwith Tracedwith Tracedwith\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Eval Greedy >> Env Steps: 0.00e+00 | Episode Length: 34.000 | Episode Return: 0.000 | Time: 0.827\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "board (128, 9, 9)\n", + "embedding (128, 800)\n", + "(128, 9, 9, 9)\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0 Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0 Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "board (128, 9, 9)\n", + "embedding (128, 800)\n", + "(128, 9, 9, 9)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Train >> Env Steps: 0.00e+00 | Advantage: -0.000 | Critic Loss: 0.000 | Entropy: 3.029 | Entropy Loss: -3.029 | Policy Loss: -0.000 | Steps Per Second: 4,394 | Time: 291.278 | Total Loss: -0.030 | Value: 0.000\n", + "INFO:root:Eval Stochastic >> Env Steps: 1.28e+06 | Episode Length: 31.475 | Episode Return: 0.000 | Time: 0.211\n", + "INFO:root:Eval Greedy >> Env Steps: 1.28e+06 | Episode Length: 32.000 | Episode Return: 0.000 | Time: 0.001\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "board (128, 9, 9)\n", + "embedding (128, 800)\n", + "(128, 9, 9, 9)\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0 Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0 Tracedwith with\n", + " val = Tracedwith\n", + " batch_dim = 0\n", + "board (128, 9, 9)\n", + "embedding (128, 800)\n", + "(128, 9, 9, 9)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Saving checkpoint...\n", + "INFO:root:Checkpoint saved at 'training_state'.\n", + "INFO:root:Closing logger...\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/hydra/main.py:79\u001b[0m, in \u001b[0;36mmain..main_decorator..decorated_main\u001b[0;34m(cfg_passthrough)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(task_function)\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorated_main\u001b[39m(cfg_passthrough: Optional[DictConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cfg_passthrough \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg_passthrough\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 81\u001b[0m args_parser \u001b[38;5;241m=\u001b[39m get_args_parser()\n", + "File \u001b[0;32m~/workspace/jumanji/jumanji/training/train.py:105\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(cfg, log_compiles)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;66;03m# Training\u001b[39;00m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m train_timer:\n\u001b[0;32m--> 105\u001b[0m training_state, metrics \u001b[38;5;241m=\u001b[39m \u001b[43mepoch_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 106\u001b[0m logger\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[1;32m 107\u001b[0m data\u001b[38;5;241m=\u001b[39mutils\u001b[38;5;241m.\u001b[39mfirst_from_device(metrics),\n\u001b[1;32m 108\u001b[0m label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 109\u001b[0m env_steps\u001b[38;5;241m=\u001b[39menv_steps,\n\u001b[1;32m 110\u001b[0m )\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/_src/api.py:2256\u001b[0m, in \u001b[0;36m_cpp_pmap..cache_miss\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(top_trace, core\u001b[38;5;241m.\u001b[39mEvalTrace):\n\u001b[1;32m 2255\u001b[0m execute \u001b[38;5;241m=\u001b[39m pxla\u001b[38;5;241m.\u001b[39mxla_pmap_impl_lazy(fun_, \u001b[38;5;241m*\u001b[39mtracers, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m-> 2256\u001b[0m out \u001b[38;5;241m=\u001b[39m map_bind_continuation(\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtracers\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2257\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2258\u001b[0m out \u001b[38;5;241m=\u001b[39m map_bind_continuation(\n\u001b[1;32m 2259\u001b[0m pxla\u001b[38;5;241m.\u001b[39mxla_pmap_p\u001b[38;5;241m.\u001b[39mprocess(top_trace, fun_, tracers, params))\n", + "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/_src/profiler.py:314\u001b[0m, in \u001b[0;36mannotate_function..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecorator_kwargs):\n\u001b[0;32m--> 314\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapper\n", + "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/interpreters/pxla.py:2071\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 2069\u001b[0m out_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_tokens(input_bufs)\n\u001b[1;32m 2070\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2071\u001b[0m out_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded_on_local_devices\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2072\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bufs \u001b[38;5;129;01min\u001b[39;00m out_bufs:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "train(cfg)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -105,7 +330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/jumanji/__init__.py b/jumanji/__init__.py index 53bd1438d..858d8fe01 100644 --- a/jumanji/__init__.py +++ b/jumanji/__init__.py @@ -42,6 +42,8 @@ kwargs={"time_limit": 20, "generator": partly_scrambled_rubiks_cube_generator}, ) +# Sudoku - the standard Sudoku puzzle with grid of size 9x9. +register(id="Sudoku-v0", entry_point="jumanji.environments:Sudoku") ### # Packing Environments diff --git a/jumanji/environments/__init__.py b/jumanji/environments/__init__.py index 68e66834b..bf72e6e42 100644 --- a/jumanji/environments/__init__.py +++ b/jumanji/environments/__init__.py @@ -18,6 +18,7 @@ from jumanji.environments.logic.game_2048.env import Game2048 from jumanji.environments.logic.minesweeper import Minesweeper from jumanji.environments.logic.rubiks_cube import RubiksCube +from jumanji.environments.logic.sudoku import Sudoku from jumanji.environments.packing import bin_pack, job_shop, knapsack from jumanji.environments.packing.bin_pack.env import BinPack from jumanji.environments.packing.job_shop.env import JobShop diff --git a/jumanji/environments/logic/sudoku/__init__.py b/jumanji/environments/logic/sudoku/__init__.py new file mode 100644 index 000000000..7f2bcfdf9 --- /dev/null +++ b/jumanji/environments/logic/sudoku/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from jumanji.environments.logic.sudoku.env import Sudoku +from jumanji.environments.logic.sudoku.types import Observation, State diff --git a/jumanji/environments/logic/sudoku/conftest.py b/jumanji/environments/logic/sudoku/conftest.py new file mode 100644 index 000000000..592f83b24 --- /dev/null +++ b/jumanji/environments/logic/sudoku/conftest.py @@ -0,0 +1,27 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import jax +import jax.numpy as jnp +import pytest + +from jumanji.environments.logic.sudoku.env import Sudoku +from jumanji.environments.logic.sudoku.generator import DummyGenerator +from jumanji.environments.logic.sudoku.types import State + + +@pytest.fixture +def sudoku_env() -> Sudoku: + """Fixture for a default sudoku environment with 10 rows and columns, and 10 mines.""" + return Sudoku(generator=DummyGenerator()) diff --git a/jumanji/environments/logic/sudoku/constants.py b/jumanji/environments/logic/sudoku/constants.py new file mode 100644 index 000000000..def31a100 --- /dev/null +++ b/jumanji/environments/logic/sudoku/constants.py @@ -0,0 +1,28 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +BOARD_WIDTH: int = 9 + + +BOX_IDX = [ + [0, 1, 2, 9, 10, 11, 18, 19, 20], + [27, 28, 29, 36, 37, 38, 45, 46, 47], + [54, 55, 56, 63, 64, 65, 72, 73, 74], + [3, 4, 5, 12, 13, 14, 21, 22, 23], + [30, 31, 32, 39, 40, 41, 48, 49, 50], + [57, 58, 59, 66, 67, 68, 75, 76, 77], + [6, 7, 8, 15, 16, 17, 24, 25, 26], + [33, 34, 35, 42, 43, 44, 51, 52, 53], + [60, 61, 62, 69, 70, 71, 78, 79, 80], +] diff --git a/jumanji/environments/logic/sudoku/env.py b/jumanji/environments/logic/sudoku/env.py new file mode 100644 index 000000000..04679f3d8 --- /dev/null +++ b/jumanji/environments/logic/sudoku/env.py @@ -0,0 +1,204 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Optional, Sequence, Tuple + +import chex +import jax +import jax.numpy as jnp +import matplotlib + +from jumanji import Environment, specs +from jumanji.environments.logic.sudoku.constants import BOARD_WIDTH +from jumanji.environments.logic.sudoku.generator import DummyGenerator, Generator + +# from jumanji.environments.logic.sudoku.specs import ObservationSpec +from jumanji.environments.logic.sudoku.types import Observation, State +from jumanji.environments.logic.sudoku.utils import ( + apply_action, + update_action_mask, + validate_board, +) +from jumanji.environments.logic.sudoku.viewer import SudokuViewer +from jumanji.types import TimeStep, restart, termination, transition +from jumanji.viewer import Viewer + + +class Sudoku(Environment[State]): + """A JAX implementation of the sudoku game. + + - observation: `Observation` + - board: jax array (int32) of shape (9,9): + empty cells are represented by -1, and filled cells are represented by 0-8. + - action_mask: jax array (bool) of shape (9,9,9): + indicates which actions are valid. + + - action: + multi discrete array containing the square to write a digit, and the digits + to input. + + - reward: jax array (float32): + 1 at the end of the episode if the board is valid + 0 otherwise + + + - state: `State` + - board: jax array (int32) of shape (9,9): + empty cells are represented by -1, and filled cells are represented by 0-8. + + - action_mask: jax array (bool) of shape (9,9,9): + indicates which actions are valid (empty cells and valid digits). + + - key: jax array (int32) of shape (2,) used for seeding initial sudoku + configuration. + + ```python + from jumanji.environments import Sudoku + env = Sudoku() + key = jax.random.key(0) + state, timestep = jax.jit(env.reset)(key) + env.render(state) + action = env.action_spec().generate_value() + state, timestep = jax.jit(env.step)(state, action) + env.render(state) + ``` + """ + + def __init__( + self, + generator: Optional[Generator] = None, + viewer: Optional[Viewer[State]] = None, + ): + if generator is None: + generator = DummyGenerator() + + if viewer is None: + viewer = SudokuViewer() + + self._viewer = viewer + self._generator = generator + + def __repr__(self) -> str: + return f"Sudoku(grid_size={BOARD_WIDTH}x{BOARD_WIDTH})" + + def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]: + state = self._generator(key) + obs = Observation(board=state.board, action_mask=state.action_mask) + timestep = restart(observation=obs) + return state, timestep + + def step( + self, state: State, action: chex.Array + ) -> Tuple[State, TimeStep[Observation]]: + # check if action is valid + invalid = state.action_mask[action[0], action[1], action[2]] == 0 + updated_board = apply_action(action=action, board=state.board) + updated_action_mask = update_action_mask( + action_mask=state.action_mask, board=updated_board + ).astype(bool) + + winning = validate_board(updated_board) + # creating next state + next_state = State( + board=updated_board, action_mask=updated_action_mask, key=state.key + ) + + no_actions_avalaible = updated_action_mask.sum() == 0 + + # computing terminal condition + done = invalid | winning | no_actions_avalaible + reward = winning * 1.0 + + observation = Observation(board=updated_board, action_mask=updated_action_mask) + + timestep = jax.lax.cond( + done, + termination, + transition, + reward, + observation, + ) + + return next_state, timestep + + def observation_spec(self) -> specs.Spec[Observation]: + """Returns the observation spec containing the board and action_mask arrays. + + Returns: + Spec containing all the specifications for all the `Observation` fields: + - board: BoundedArray (jnp.int8) of shape (9,9). + - action_mask: BoundedArray (bool) of shape (9,9,9). + """ + + board = specs.BoundedArray( + shape=(BOARD_WIDTH, BOARD_WIDTH), + minimum=-1, + maximum=BOARD_WIDTH, + dtype=jnp.int32, + name="board", + ) + + action_mask = specs.BoundedArray( + shape=(BOARD_WIDTH, BOARD_WIDTH, BOARD_WIDTH), + dtype=bool, + minimum=0, + maximum=1, + name="action_mask", + ) + + return specs.Spec( + Observation, "ObservationSpec", board=board, action_mask=action_mask + ) + + def action_spec(self) -> specs.MultiDiscreteArray: + """Returns the action spec. An action is composed of 3 integers: the row index, + the column index and the value to be placed in the cell. + + Returns: + action_spec: `MultiDiscreteArray` object. + """ + return specs.MultiDiscreteArray( + num_values=jnp.array([BOARD_WIDTH, BOARD_WIDTH, BOARD_WIDTH], int), + name="action", + dtype=jnp.int32, + ) + + def render(self, state: State) -> Any: + """Renders the current state of the sudoku. + + Args: + state: the current state to be rendered. + """ + return self._viewer.render(state=state) + + def animate( + self, + states: Sequence[State], + interval: int = 200, + save_path: Optional[str] = None, + ) -> matplotlib.animation.FuncAnimation: + """Creates an animated gif of the board based on the sequence of states. + + Args: + states: a list of `State` objects representing the sequence of states. + interval: the delay between frames in milliseconds, default to 200. + save_path: the path where the animation file should be saved. If it is None, the plot + will not be saved. + + Returns: + animation.FuncAnimation: the animation object that was created. + """ + return self._viewer.animate( + states=states, interval=interval, save_path=save_path + ) diff --git a/jumanji/environments/logic/sudoku/env_test.py b/jumanji/environments/logic/sudoku/env_test.py new file mode 100644 index 000000000..328abe145 --- /dev/null +++ b/jumanji/environments/logic/sudoku/env_test.py @@ -0,0 +1,93 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import chex +import jax +import matplotlib.animation +import matplotlib.pyplot as plt +import pytest +import pytest_mock +from jax import numpy as jnp + +from jumanji.environments.logic.sudoku.env import Sudoku +from jumanji.environments.logic.sudoku.types import State +from jumanji.testing.env_not_smoke import check_env_does_not_smoke +from jumanji.testing.pytrees import assert_is_jax_array_tree +from jumanji.types import TimeStep + + +def test_sudoku__reset(sudoku_env: Sudoku) -> None: + """Validates the jitted reset of the environment.""" + reset_fn = jax.jit(sudoku_env.reset) + key = jax.random.PRNGKey(0) + state, timestep = reset_fn(key) + assert isinstance(timestep, TimeStep) + assert isinstance(state, State) + + assert jnp.array_equal(state.board, timestep.observation.board) + # Check that the state is made of DeviceArrays, this is false for the non-jitted + # reset function since unpacking random.split returns numpy arrays and not device arrays. + assert_is_jax_array_tree(state) + + +def test_sudoku__step(sudoku_env: Sudoku) -> None: + """Validates the jitted step of the environment.""" + chex.clear_trace_counter() + step_fn = chex.assert_max_traces(sudoku_env.step, n=2) + step_fn = jax.jit(step_fn) + key = jax.random.PRNGKey(0) + state, timestep = jax.jit(sudoku_env.reset)(key) + # For this board, this action will be a non-mined square + action = sudoku_env.action_spec().generate_value() + next_state, next_timestep = step_fn(state, action) + + # Check that the state has changed + assert not jnp.array_equal(next_state.board, state.board) + assert jnp.array_equal(next_state.board, next_timestep.observation.board) + + # Check that the state is made of DeviceArrays, this is false for the non-jitted + # step function since unpacking random.split returns numpy arrays and not device arrays. + assert_is_jax_array_tree(next_state) + + next_next_state, next_next_timestep = step_fn(next_state, action) + + # Check that the state has changed, since we took the same action twice + assert jnp.array_equal(next_next_state.board, next_next_timestep.observation.board) + + +def test_sudoku__does_not_smoke(sudoku_env: Sudoku) -> None: + """Test that we can run an episode without any errors.""" + check_env_does_not_smoke(env=sudoku_env) + + +def test_sudoku__render(monkeypatch: pytest.MonkeyPatch, sudoku_env: Sudoku) -> None: + """Check that the render method builds the figure but does not display it.""" + monkeypatch.setattr(plt, "show", lambda fig: None) + state, timestep = jax.jit(sudoku_env.reset)(jax.random.PRNGKey(0)) + sudoku_env.render(state) + sudoku_env.close() + action = sudoku_env.action_spec().generate_value() + state, timestep = jax.jit(sudoku_env.step)(state, action) + sudoku_env.render(state) + sudoku_env.close() + + +def test_sudoku_animation( + sudoku_env: Sudoku, mocker: pytest_mock.MockerFixture +) -> None: + """Check that the animation method creates the animation correctly.""" + states = mocker.MagicMock() + animation = sudoku_env.animate(states) + assert isinstance(animation, matplotlib.animation.Animation) diff --git a/jumanji/environments/logic/sudoku/generator.py b/jumanji/environments/logic/sudoku/generator.py new file mode 100644 index 000000000..d04a3d9e8 --- /dev/null +++ b/jumanji/environments/logic/sudoku/generator.py @@ -0,0 +1,84 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.i + +import abc + +import chex +import jax.numpy as jnp +import numpy as np + +from jumanji.environments.logic.sudoku.constants import BOARD_WIDTH +from jumanji.environments.logic.sudoku.types import State +from jumanji.environments.logic.sudoku.utils import update_action_mask + + +class Generator(abc.ABC): + @abc.abstractmethod + def __call__(self, key: chex.PRNGKey) -> State: + """Call method responsible for generating a new state. + + Args: + key: jax random key for any stochasticity used in the instance + generation process. + + Returns: + A Sudoku State. + """ + pass + + +# class CSVGenerator(Generator): +# def __init__(self, data_path: Path): +# self.data_path = data_path +# board = np.loadtxt(self.data_path, delimiter=",") +# action_mask = np.ma.make_mask(board) +# board = jnp.array(board, dtype=jnp.int8) - 1 +# action_mask = jnp.array(action_mask, dtype=jnp.int8) +# action_mask = 1 - jnp.expand_dims(action_mask, -1).repeat(BOARD_WIDTH, axis=-1) +# action_mask = update_action_mask(action_mask, board) + +# self.state = State(board=board, action_mask=action_mask) + +# def __call__(self, key: PRNGKey): +# return self.state + + +class DummyGenerator(Generator): + def __init__( + self, + ): + board = jnp.array( + [ + [7, 8, 0, 4, 0, 0, 1, 2, 0], + [6, 0, 0, 0, 7, 5, 0, 0, 9], + [0, 0, 0, 6, 0, 1, 0, 7, 8], + [0, 0, 7, 0, 4, 0, 2, 6, 0], + [0, 0, 1, 0, 5, 0, 9, 3, 0], + [9, 0, 4, 0, 6, 0, 0, 0, 5], + [0, 7, 0, 3, 0, 0, 0, 1, 2], + [1, 2, 0, 0, 0, 7, 4, 0, 0], + [0, 4, 9, 2, 0, 6, 0, 0, 7], + ] + ) + + action_mask = np.ma.make_mask(board) + board = jnp.array(board, dtype=jnp.int32) - 1 + action_mask = jnp.array(action_mask, dtype=jnp.int32) + action_mask = 1 - jnp.expand_dims(action_mask, -1).repeat(BOARD_WIDTH, axis=-1) + action_mask = update_action_mask(action_mask, board).astype(bool) + self._board = board + self._action_mask = action_mask + + def __call__(self, key: chex.PRNGKey): + return State(board=self._board, action_mask=self._action_mask, key=key) diff --git a/jumanji/environments/logic/sudoku/types.py b/jumanji/environments/logic/sudoku/types.py new file mode 100644 index 000000000..f1fdd3221 --- /dev/null +++ b/jumanji/environments/logic/sudoku/types.py @@ -0,0 +1,45 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING, NamedTuple + +import chex + +if TYPE_CHECKING: + from dataclasses import dataclass +else: + from chex import dataclass + + +@dataclass +class State: + """ + board: 2D-array of integers representing the board. + action_mask: 3D-array of booleans that indicates valid actions. + key: random key used for auto-reset. + """ + + board: chex.Array # (board_size, board_size) + action_mask: chex.Array # (board_size, board_size, board_size) + key: chex.PRNGKey # # (2,). + + +class Observation(NamedTuple): + """ + board: 2D-array of integers representing the board. + action_mask: 3D-array of booleans that indicates valid actions. + """ + + board: chex.Array # (board_size, board_size) + action_mask: chex.Array # (board_size, board_size, board_size) diff --git a/jumanji/environments/logic/sudoku/utils.py b/jumanji/environments/logic/sudoku/utils.py new file mode 100644 index 000000000..3d1b5e19b --- /dev/null +++ b/jumanji/environments/logic/sudoku/utils.py @@ -0,0 +1,123 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import chex +import jax +import jax.numpy as jnp + +from jumanji.environments.logic.sudoku.constants import BOARD_WIDTH, BOX_IDX + +# def create_board_csv(): +# board = [ +# [7, 8, 0, 4, 0, 0, 1, 2, 0], +# [6, 0, 0, 0, 7, 5, 0, 0, 9], +# [0, 0, 0, 6, 0, 1, 0, 7, 8], +# [0, 0, 7, 0, 4, 0, 2, 6, 0], +# [0, 0, 1, 0, 5, 0, 9, 3, 0], +# [9, 0, 4, 0, 6, 0, 0, 0, 5], +# [0, 7, 0, 3, 0, 0, 0, 1, 2], +# [1, 2, 0, 0, 0, 7, 4, 0, 0], +# [0, 4, 9, 2, 0, 6, 0, 0, 7], +# ] +# np.savetxt("test_puzzle.csv", board, delimiter=",") + + +# def create_board_csv(): +# board = [ +# [0, 0, 0, 8, 0, 1, 0, 0, 0], +# [0, 0, 0, 0, 0, 0, 0, 4, 3], +# [5, 0, 0, 0, 0, 0, 0, 0, 0], +# [0, 0, 0, 0, 7, 0, 8, 0, 0], +# [0, 0, 0, 0, 0, 0, 1, 0, 0], +# [0, 2, 0, 0, 3, 0, 0, 0, 0], +# [6, 0, 0, 0, 0, 0, 0, 7, 5], +# [0, 0, 3, 4, 0, 0, 0, 0, 0], +# [0, 0, 0, 2, 0, 0, 6, 0, 0], +# ] +# np.savetxt("test_puzzle.csv", board, delimiter=",") + +# solved_board = [ +# [2, 3, 7, 8, 4, 1, 5, 6, 9], +# [1, 8, 6, 7, 9, 5, 2, 4, 3], +# [5, 9, 4, 3, 2, 6, 7, 1, 8], +# [3, 1, 5, 6, 7, 4, 8, 9, 2], +# [4, 6, 9, 5, 8, 2, 1, 3, 7], +# [7, 2, 8, 1, 3, 9, 4, 5, 6], +# [6, 4, 2, 9, 1, 8, 3, 7, 5], +# [8, 5, 3, 4, 6, 7, 9, 2, 1], +# [9, 7, 1, 2, 5, 3, 6, 8, 4], +# ] + +# np.savetxt("test_solution.csv", solved_board, delimiter=",") + + +def apply_action(action: chex.Array, board: chex.Array) -> chex.Array: + return board.at[action[0], action[1]].set(action[2]) + + +def validate_board(board: chex.Array) -> chex.Array: + """Checks that every row, column and 3x3 boxes includes all figures. + + Args: + board: The sudoku board. + + Returns: + condition: A `bool` indicator that validates a solution or rejects it. + """ + + def _validate_row(row: chex.Array) -> chex.Array: + condition = jnp.nonzero(row, size=BOARD_WIDTH)[0] == jnp.arange(BOARD_WIDTH) + return condition.all() + + condition_rows = jax.vmap(_validate_row)(board).all() + condition_columns = jax.vmap(_validate_row)(board.T).all() + condition_boxes = jax.vmap(_validate_row)(jnp.array(BOX_IDX)).all() + + return condition_rows & condition_columns & condition_boxes + + +def update_action_mask(action_mask: chex.Array, board: chex.Array) -> chex.Array: + """Updates the action mask according to the current board state. + + Args: + action_mask: The action mask. + board: The sudoku board. + + Returns: + The updated action mask. + """ + row_mask = 1 - jax.nn.one_hot(board, BOARD_WIDTH).any(axis=1) * 1 + column_mask = 1 - jax.nn.one_hot(board.T, BOARD_WIDTH).any(axis=1) * 1 + + boxes = board.reshape(BOARD_WIDTH**2).take(jnp.array(BOX_IDX)) + box_mask = 1 - jax.nn.one_hot(boxes, BOARD_WIDTH).any(axis=1) * 1 + + boxes_action_mask = action_mask.reshape(BOARD_WIDTH**2, BOARD_WIDTH)[ + jnp.array(BOX_IDX) + ] + boxes_action_mask *= box_mask.reshape(BOARD_WIDTH, 1, BOARD_WIDTH) + + action_mask = ( + action_mask.reshape(BOARD_WIDTH**2, BOARD_WIDTH) + .at[jnp.array(BOX_IDX)] + .set(boxes_action_mask) + .reshape(BOARD_WIDTH, BOARD_WIDTH, BOARD_WIDTH) + ) + action_mask *= row_mask.reshape(BOARD_WIDTH, 1, BOARD_WIDTH) + action_mask *= column_mask.reshape(1, BOARD_WIDTH, BOARD_WIDTH) + board_mask = (board == -1) * 1 + + action_mask *= board_mask.reshape(BOARD_WIDTH, BOARD_WIDTH, 1) + + return action_mask diff --git a/jumanji/environments/logic/sudoku/viewer.py b/jumanji/environments/logic/sudoku/viewer.py new file mode 100644 index 000000000..ecc239495 --- /dev/null +++ b/jumanji/environments/logic/sudoku/viewer.py @@ -0,0 +1,167 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional, Sequence, Tuple + +import matplotlib +import matplotlib.animation as animation +import matplotlib.pyplot as plt + +import jumanji +from jumanji.environments.logic.sudoku.constants import BOARD_WIDTH +from jumanji.environments.logic.sudoku.env import State +from jumanji.viewer import Viewer + + +class SudokuViewer(Viewer[State]): + def __init__( + self, + name: str = "Sudoku", + ) -> None: + self._name = name + self._animation: Optional[animation.Animation] = None + + def render( + self, + state: State, + save_path: Optional[str] = None, + ax: Optional[plt.Axes] = None, + ) -> None: + self._clear_display() + + if ax is None: + fig, ax = plt.subplots(figsize=(6, 6)) + plt.title(f"{self._name}") + else: + fig = ax.figure + ax.clear() + self._draw(ax, state) + ax.text( + -0.5, -0.5, f"Remaining actions: {state.action_mask.sum()}", fontsize=14 + ) + self._display_human(fig) + + def _draw_board(self, ax: plt.Axes) -> None: + # Draw the square box that delimits the board. + ax.axis("off") + lines = [] + + _linewidth = 2.5 + ax.plot([0, 0], [0, BOARD_WIDTH], "-k", lw=_linewidth) + ax.plot([0, BOARD_WIDTH], [BOARD_WIDTH, BOARD_WIDTH], "-k", lw=_linewidth) + ax.plot([BOARD_WIDTH, BOARD_WIDTH], [BOARD_WIDTH, 0], "-k", lw=_linewidth) + ax.plot([BOARD_WIDTH, 0], [0, 0], "-k", lw=_linewidth) + for i in range(1, BOARD_WIDTH): + if i % int(BOARD_WIDTH**0.5) == 0: + _linewidth = 2.5 + else: + _linewidth = 1 + + hline = matplotlib.lines.Line2D( + [0, BOARD_WIDTH], [i, i], color="k", linewidth=_linewidth + ) + vline = matplotlib.lines.Line2D( + [i, i], [0, BOARD_WIDTH], color="k", linewidth=_linewidth + ) + + lines.append(hline) + lines.append(vline) + + for line in lines: + ax.add_line(line) + + def animate( + self, + states: Sequence[State], + interval: int = 500, + save_path: Optional[str] = None, + ) -> animation.FuncAnimation: + fig, ax = plt.subplots(figsize=(6, 6)) + plt.title(f"{self._name}") + + def make_frame(state_index: int) -> None: + state = states[state_index] + self.render(state, ax=ax) + + _animation = animation.FuncAnimation( + fig, make_frame, frames=len(states), interval=interval, blit=False + ) + + if save_path: + _animation.save(save_path) + + return _animation + + def _get_fig_ax(self) -> Tuple[plt.Figure, plt.Axes]: + exists = plt.fignum_exists(self._name) + if exists: + fig = plt.figure(self._name) + ax = fig.get_axes()[0] + else: + fig = plt.figure( + self._name, + figsize=(6, 6), + ) + fig.set_tight_layout({"pad": False, "w_pad": 0.0, "h_pad": 0.0}) + if not plt.isinteractive(): + fig.show() + ax = fig.add_subplot() + return fig, ax + + def close(self) -> None: + plt.close(self._name) + + def _draw(self, ax: plt.Axes, state: State) -> None: + ax.clear() + self._draw_board(ax) + self._draw_figures(ax, state) + + def _draw_figures(self, ax, state: State) -> None: + """Loop over the different cells and draws corresponding shapes in the ax object.""" + board = state.board + board_shape = board.shape + + for i in range(board_shape[0]): + for j in range(board_shape[1]): + x_pos = j + 0.5 + y_pos = board_shape[0] - i - 0.5 + element = board[i, j] + if element != -1: + ax.text( + x_pos, + y_pos, + element + 1, + horizontalalignment="center", + verticalalignment="center", + fontsize=16, + ) + + return + + def _display_human(self, fig: plt.Figure) -> None: + if plt.isinteractive(): + # Required to update render when using Jupyter Notebook. + fig.canvas.draw() + if jumanji.environments.is_colab(): + plt.show(self._name) + else: + # Required to update render when not using Jupyter Notebook. + fig.canvas.draw_idle() + fig.canvas.flush_events() + + def _clear_display(self) -> None: + if jumanji.environments.is_colab(): + import IPython.display + + IPython.display.clear_output(True) diff --git a/jumanji/training/configs/config.yaml b/jumanji/training/configs/config.yaml index 34d88409c..79ee4e830 100644 --- a/jumanji/training/configs/config.yaml +++ b/jumanji/training/configs/config.yaml @@ -1,6 +1,6 @@ defaults: - _self_ - - env: snake # [bin_pack, cleaner, connector, cvrp, game_2048, job_shop, knapsack, maze, minesweeper, rubiks_cube, snake, tsp] + - env: snake # [bin_pack, cleaner, connector, cvrp, game_2048, job_shop, knapsack, maze, minesweeper, rubiks_cube, snake, sudoku, tsp] agent: random # [random, a2c] diff --git a/jumanji/training/networks/__init__.py b/jumanji/training/networks/__init__.py index 0b24bb2fe..82a67e102 100644 --- a/jumanji/training/networks/__init__.py +++ b/jumanji/training/networks/__init__.py @@ -56,5 +56,9 @@ make_actor_critic_networks_snake, ) from jumanji.training.networks.snake.random import make_random_policy_snake +from jumanji.training.networks.sudoku.actor_critic import ( + make_actor_critic_networks_sudoku, +) +from jumanji.training.networks.sudoku.random import make_random_policy_sudoku from jumanji.training.networks.tsp.actor_critic import make_actor_critic_networks_tsp from jumanji.training.networks.tsp.random import make_random_policy_tsp diff --git a/jumanji/training/networks/sudoku/__init__.py b/jumanji/training/networks/sudoku/__init__.py new file mode 100644 index 000000000..21db9ec1c --- /dev/null +++ b/jumanji/training/networks/sudoku/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/jumanji/training/networks/sudoku/actor_critic.py b/jumanji/training/networks/sudoku/actor_critic.py new file mode 100644 index 000000000..000e666d8 --- /dev/null +++ b/jumanji/training/networks/sudoku/actor_critic.py @@ -0,0 +1,95 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Sequence + +import chex +import haiku as hk +import jax +import jax.numpy as jnp +import numpy as np + +from jumanji.environments.logic.sudoku import Observation, Sudoku +from jumanji.environments.logic.sudoku.constants import BOARD_WIDTH +from jumanji.training.networks.actor_critic import ( + ActorCriticNetworks, + FeedForwardNetwork, +) +from jumanji.training.networks.parametric_distribution import ( + FactorisedActionSpaceParametricDistribution, +) + + +def make_actor_critic_networks_sudoku( + sudoku: Sudoku, + num_channels: int, + policy_layers: Sequence[int], + value_layers: Sequence[int], +) -> ActorCriticNetworks: + """Make actor-critic networks for the `Sudoku` environment.""" + num_actions = sudoku.action_spec().num_values + parametric_action_distribution = FactorisedActionSpaceParametricDistribution( + action_spec_num_values=np.asarray(num_actions) + ) + + policy_network = make_sudoku_cnn( + num_outputs=int(np.prod(num_actions)), + mlp_units=policy_layers, + conv_n_channels=num_channels, + ) + value_network = make_sudoku_cnn( + num_outputs=1, + mlp_units=value_layers, + conv_n_channels=num_channels, + ) + return ActorCriticNetworks( + policy_network=policy_network, + value_network=value_network, + parametric_action_distribution=parametric_action_distribution, + ) + + +def make_sudoku_cnn( + num_outputs: int, + mlp_units: Sequence[int], + conv_n_channels: int, +) -> FeedForwardNetwork: + def network_fn(observation: Observation) -> chex.Array: + torso = hk.Sequential( + [ + hk.Conv2D(conv_n_channels, (2, 2), 2), + jax.nn.relu, + hk.Conv2D(conv_n_channels, (2, 2), 1), + jax.nn.relu, + hk.Flatten(), + ] + ) + embedding = torso(observation.board[..., None] / BOARD_WIDTH - 0.5) + + head = hk.nets.MLP((*mlp_units, num_outputs), activate_final=False) + if num_outputs == 1: + value = jnp.squeeze(head(embedding), axis=-1) + return value + else: + logits = head(embedding) + logits = logits.reshape(-1, BOARD_WIDTH, BOARD_WIDTH, BOARD_WIDTH) + + logits = jnp.where( + observation.action_mask, logits, jnp.finfo(jnp.float32).min + ) + + return logits.reshape(observation.action_mask.shape[0], -1) + + init, apply = hk.without_apply_rng(hk.transform(network_fn)) + return FeedForwardNetwork(init=init, apply=apply) diff --git a/jumanji/training/networks/sudoku/random.py b/jumanji/training/networks/sudoku/random.py new file mode 100644 index 000000000..ff858d9c1 --- /dev/null +++ b/jumanji/training/networks/sudoku/random.py @@ -0,0 +1,62 @@ +# Copyright 2022 InstaDeep Ltd. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import chex +import jax +import jax.numpy as jnp + +from jumanji.environments.logic.sudoku import Sudoku +from jumanji.training.networks.masked_categorical_random import ( + ObservationWithActionMask, + make_masked_categorical_random_ndim, +) +from jumanji.training.networks.protocols import RandomPolicy + +# def make_random_policy_sudoku() -> RandomPolicy: +# """Make random policy for the `Sudoku` environment.""" + +# def vmap_random_policy_sudoku( +# observation: ObservationWithActionMask, +# key: chex.PRNGKey, +# ) -> chex.Array: +# keys = jax.random.split(key, observation.action_mask.shape[0]) + +# return jax.vmap(random_policy_sudoku)(observation, keys) + +# return vmap_random_policy_sudoku + + +# def random_policy_sudoku( +# observation: ObservationWithActionMask, +# key: chex.PRNGKey, +# ) -> chex.Array: +# raveled_mask = observation.action_mask.ravel() +# idx = jax.random.choice( +# key, +# jnp.arange(len(raveled_mask)), +# shape=(1,), +# p=raveled_mask / raveled_mask.sum(), +# )[0] +# unravel_idx = jnp.unravel_index(idx, observation.action_mask.shape) +# action = jnp.stack(unravel_idx) + +# return action +def make_random_policy_sudoku(sudoku: Sudoku) -> RandomPolicy: + """Make random policy for the `Sudoku` environment.""" + + action_spec_num_values = sudoku.action_spec().num_values + + return make_masked_categorical_random_ndim( + action_spec_num_values=action_spec_num_values + ) diff --git a/jumanji/training/setup_train.py b/jumanji/training/setup_train.py index fb4cf3634..4c8d87506 100644 --- a/jumanji/training/setup_train.py +++ b/jumanji/training/setup_train.py @@ -35,6 +35,7 @@ Minesweeper, RubiksCube, Snake, + Sudoku, ) from jumanji.training import networks from jumanji.training.agents.a2c import A2CAgent @@ -158,6 +159,9 @@ def _setup_random_policy( # noqa: CCR001 elif cfg.env.name == "game_2048": assert isinstance(env.unwrapped, Game2048) random_policy = networks.make_random_policy_game_2048() + elif cfg.env.name == "sudoku": + assert isinstance(env.unwrapped, Sudoku) + random_policy = networks.make_random_policy_sudoku(sudoku=env.unwrapped) elif cfg.env.name == "cleaner": assert isinstance(env.unwrapped, Cleaner) random_policy = networks.make_random_policy_cleaner() @@ -241,6 +245,7 @@ def _setup_actor_critic_neworks( # noqa: CCR001 policy_layers=cfg.env.network.policy_layers, value_layers=cfg.env.network.value_layers, ) + elif cfg.env.name == "rubiks_cube": assert isinstance(env.unwrapped, RubiksCube) actor_critic_networks = networks.make_actor_critic_networks_rubiks_cube( @@ -249,6 +254,15 @@ def _setup_actor_critic_neworks( # noqa: CCR001 step_count_embed_dim=cfg.env.network.step_count_embed_dim, dense_layer_dims=cfg.env.network.dense_layer_dims, ) + + elif cfg.env.name == "sudoku": + assert isinstance(env.unwrapped, Sudoku) + actor_critic_networks = networks.make_actor_critic_networks_sudoku( + sudoku=env.unwrapped, + num_channels=cfg.env.network.num_channels, + policy_layers=cfg.env.network.policy_layers, + value_layers=cfg.env.network.value_layers, + ) elif cfg.env.name == "minesweeper": assert isinstance(env.unwrapped, Minesweeper) actor_critic_networks = networks.make_actor_critic_networks_minesweeper( From 1c139a450da7d7977a1139586ab9f55308ec77ca Mon Sep 17 00:00:00 2001 From: Raphael Boige Date: Thu, 27 Apr 2023 20:02:58 +0200 Subject: [PATCH 02/55] feat: make generator configurable and add puzzles data --- examples/training.ipynb | 143 +++++------------- jumanji/__init__.py | 14 +- .../logic/sudoku/data/10000_mixed_puzzles.npy | Bin 0 -> 810128 bytes .../logic/sudoku/data/10_easy_puzzles.npy | Bin 0 -> 8228 bytes jumanji/environments/logic/sudoku/env.py | 4 +- .../environments/logic/sudoku/generator.py | 67 ++++++-- 6 files changed, 108 insertions(+), 120 deletions(-) create mode 100644 jumanji/environments/logic/sudoku/data/10000_mixed_puzzles.npy create mode 100644 jumanji/environments/logic/sudoku/data/10_easy_puzzles.npy diff --git a/examples/training.ipynb b/examples/training.ipynb index 18d39ae83..e56af69b8 100644 --- a/examples/training.ipynb +++ b/examples/training.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "tags": [] }, @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { @@ -81,10 +81,10 @@ { "data": { "text/plain": [ - "{'agent': 'a2c', 'seed': 0, 'logger': {'type': 'terminal', 'save_checkpoint': True, 'name': '${agent}_${env.name}'}, 'env': {'name': 'sudoku', 'registered_version': 'Sudoku-v0', 'network': {'num_channels': 32, 'policy_layers': [64, 64], 'value_layers': [128, 128]}, 'training': {'num_epochs': 100, 'num_learner_steps_per_epoch': 500, 'n_steps': 20, 'total_batch_size': 128}, 'evaluation': {'eval_total_batch_size': 200, 'greedy_eval_total_batch_size': 200}, 'a2c': {'normalize_advantage': False, 'discount_factor': 0.997, 'bootstrapping_factor': 0.95, 'l_pg': 1.0, 'l_td': 1.0, 'l_en': 0.01, 'learning_rate': 0.0004}}}" + "{'agent': 'a2c', 'seed': 0, 'logger': {'type': 'terminal', 'save_checkpoint': True, 'name': '${agent}_${env.name}'}, 'env': {'name': 'sudoku', 'registered_version': 'Sudoku-very-easy-v0', 'network': {'num_channels': 32, 'policy_layers': [64, 64], 'value_layers': [128, 128]}, 'training': {'num_epochs': 100, 'num_learner_steps_per_epoch': 500, 'n_steps': 20, 'total_batch_size': 128}, 'evaluation': {'eval_total_batch_size': 200, 'greedy_eval_total_batch_size': 200}, 'a2c': {'normalize_advantage': False, 'discount_factor': 0.997, 'bootstrapping_factor': 0.95, 'l_pg': 1.0, 'l_td': 1.0, 'l_en': 0.01, 'learning_rate': 0.0004}}}" ] }, - "execution_count": 28, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "tags": [] }, @@ -122,7 +122,7 @@ " name: ${agent}_${env.name}\n", "env:\n", " name: sudoku\n", - " registered_version: Sudoku-v0\n", + " registered_version: Sudoku-very-easy-v0\n", " network:\n", " num_channels: 32\n", " policy_layers:\n", @@ -156,12 +156,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "board (1, 9, 9)\n", - "embedding (1, 800)\n", - "(1, 9, 9, 9)\n", - "board (1, 9, 9)\n", - "embedding (1, 800)\n", - "(1, 9, 9, 9)\n" + "(9, 9)\n", + "(9, 9)\n" ] }, { @@ -175,123 +171,68 @@ "name": "stdout", "output_type": "stream", "text": [ - "board (1, 9, 9)\n", - "embedding (1, 800)\n", - "(1, 9, 9, 9)\n", - "Tracedwith\n", - "Tracedwith\n", - "Tracedwith Tracedwith Tracedwith\n" + "(9, 9)\n", + "(9, 9)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Eval Stochastic >> Env Steps: 0.00e+00 | Episode Length: 31.815 | Episode Return: 0.000 | Time: 1.501\n" + "INFO:root:Eval Stochastic >> Env Steps: 0.00e+00 | Episode Length: 10.660 | Episode Return: 0.000 | Time: 1.882\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "board (1, 9, 9)\n", - "embedding (1, 800)\n", - "(1, 9, 9, 9)\n", - "Tracedwith\n", - "Tracedwith\n", - "Tracedwith Tracedwith Tracedwith\n" + "(9, 9)\n", + "(9, 9)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Eval Greedy >> Env Steps: 0.00e+00 | Episode Length: 34.000 | Episode Return: 0.000 | Time: 0.827\n" + "INFO:root:Eval Greedy >> Env Steps: 0.00e+00 | Episode Length: 10.695 | Episode Return: 0.000 | Time: 1.582\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "board (128, 9, 9)\n", - "embedding (128, 800)\n", - "(128, 9, 9, 9)\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0 Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0 Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "board (128, 9, 9)\n", - "embedding (128, 800)\n", - "(128, 9, 9, 9)\n" + "(9, 9)\n", + "(9, 9)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Train >> Env Steps: 0.00e+00 | Advantage: -0.000 | Critic Loss: 0.000 | Entropy: 3.029 | Entropy Loss: -3.029 | Policy Loss: -0.000 | Steps Per Second: 4,394 | Time: 291.278 | Total Loss: -0.030 | Value: 0.000\n", - "INFO:root:Eval Stochastic >> Env Steps: 1.28e+06 | Episode Length: 31.475 | Episode Return: 0.000 | Time: 0.211\n", - "INFO:root:Eval Greedy >> Env Steps: 1.28e+06 | Episode Length: 32.000 | Episode Return: 0.000 | Time: 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "board (128, 9, 9)\n", - "embedding (128, 800)\n", - "(128, 9, 9, 9)\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0 Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0 Tracedwith with\n", - " val = Tracedwith\n", - " batch_dim = 0\n", - "board (128, 9, 9)\n", - "embedding (128, 800)\n", - "(128, 9, 9, 9)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Saving checkpoint...\n", - "INFO:root:Checkpoint saved at 'training_state'.\n", - "INFO:root:Closing logger...\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/hydra/main.py:79\u001b[0m, in \u001b[0;36mmain..main_decorator..decorated_main\u001b[0;34m(cfg_passthrough)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(task_function)\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorated_main\u001b[39m(cfg_passthrough: Optional[DictConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cfg_passthrough \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg_passthrough\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 81\u001b[0m args_parser \u001b[38;5;241m=\u001b[39m get_args_parser()\n", - "File \u001b[0;32m~/workspace/jumanji/jumanji/training/train.py:105\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(cfg, log_compiles)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;66;03m# Training\u001b[39;00m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m train_timer:\n\u001b[0;32m--> 105\u001b[0m training_state, metrics \u001b[38;5;241m=\u001b[39m \u001b[43mepoch_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 106\u001b[0m logger\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[1;32m 107\u001b[0m data\u001b[38;5;241m=\u001b[39mutils\u001b[38;5;241m.\u001b[39mfirst_from_device(metrics),\n\u001b[1;32m 108\u001b[0m label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 109\u001b[0m env_steps\u001b[38;5;241m=\u001b[39menv_steps,\n\u001b[1;32m 110\u001b[0m )\n", - " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/_src/api.py:2256\u001b[0m, in \u001b[0;36m_cpp_pmap..cache_miss\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(top_trace, core\u001b[38;5;241m.\u001b[39mEvalTrace):\n\u001b[1;32m 2255\u001b[0m execute \u001b[38;5;241m=\u001b[39m pxla\u001b[38;5;241m.\u001b[39mxla_pmap_impl_lazy(fun_, \u001b[38;5;241m*\u001b[39mtracers, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m-> 2256\u001b[0m out \u001b[38;5;241m=\u001b[39m map_bind_continuation(\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtracers\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2257\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2258\u001b[0m out \u001b[38;5;241m=\u001b[39m map_bind_continuation(\n\u001b[1;32m 2259\u001b[0m pxla\u001b[38;5;241m.\u001b[39mxla_pmap_p\u001b[38;5;241m.\u001b[39mprocess(top_trace, fun_, tracers, params))\n", - "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/_src/profiler.py:314\u001b[0m, in \u001b[0;36mannotate_function..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecorator_kwargs):\n\u001b[0;32m--> 314\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapper\n", - "File \u001b[0;32m~/miniconda3/envs/qdaxpy310/lib/python3.10/site-packages/jax/interpreters/pxla.py:2071\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 2069\u001b[0m out_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_tokens(input_bufs)\n\u001b[1;32m 2070\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2071\u001b[0m out_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded_on_local_devices\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2072\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bufs \u001b[38;5;129;01min\u001b[39;00m out_bufs:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "2023-04-27 19:43:08.152712: E external/org_tensorflow/tensorflow/compiler/xla/service/slow_operation_alarm.cc:65] Constant folding an instruction is taking > 1s:\n", + "\n", + " pad.62 (displaying the full instruction incurs a runtime overhead. Raise your logging level to 4 or above).\n", + "\n", + "This isn't necessarily a bug; constant-folding is inherently a trade-off between compilation time and speed at runtime. XLA has some guards that attempt to keep constant folding from taking too long, but fundamentally you'll always be able to come up with an input program that takes a long time.\n", + "\n", + "If you'd like to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n", + "2023-04-27 19:43:08.153041: E external/org_tensorflow/tensorflow/compiler/xla/service/slow_operation_alarm.cc:133] The operation took 1.374108821s\n", + "Constant folding an instruction is taking > 1s:\n", + "\n", + " pad.62 (displaying the full instruction incurs a runtime overhead. Raise your logging level to 4 or above).\n", + "\n", + "This isn't necessarily a bug; constant-folding is inherently a trade-off between compilation time and speed at runtime. XLA has some guards that attempt to keep constant folding from taking too long, but fundamentally you'll always be able to come up with an input program that takes a long time.\n", + "\n", + "If you'd like to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n", + "INFO:root:Train >> Env Steps: 0.00e+00 | Advantage: 0.000 | Critic Loss: 0.000 | Entropy: 1.596 | Entropy Loss: -1.596 | Policy Loss: -0.000 | Steps Per Second: 4,025 | Time: 317.950 | Total Loss: -0.016 | Value: -0.000\n", + "INFO:root:Eval Stochastic >> Env Steps: 1.28e+06 | Episode Length: 10.580 | Episode Return: 0.000 | Time: 0.068\n", + "INFO:root:Eval Greedy >> Env Steps: 1.28e+06 | Episode Length: 10.615 | Episode Return: 0.000 | Time: 0.030\n", + "INFO:root:Train >> Env Steps: 1.28e+06 | Advantage: -0.000 | Critic Loss: 0.000 | Entropy: 1.595 | Entropy Loss: -1.595 | Policy Loss: -0.000 | Steps Per Second: 3,807 | Time: 336.170 | Total Loss: -0.016 | Value: -0.000\n", + "INFO:root:Eval Stochastic >> Env Steps: 2.56e+06 | Episode Length: 10.620 | Episode Return: 0.000 | Time: 0.066\n", + "INFO:root:Eval Greedy >> Env Steps: 2.56e+06 | Episode Length: 10.700 | Episode Return: 0.000 | Time: 0.029\n", + "INFO:root:Train >> Env Steps: 2.56e+06 | Advantage: -0.000 | Critic Loss: 0.000 | Entropy: 1.595 | Entropy Loss: -1.595 | Policy Loss: -0.000 | Steps Per Second: 4,026 | Time: 317.880 | Total Loss: -0.016 | Value: 0.000\n", + "INFO:root:Eval Stochastic >> Env Steps: 3.84e+06 | Episode Length: 10.665 | Episode Return: 0.000 | Time: 0.066\n", + "INFO:root:Eval Greedy >> Env Steps: 3.84e+06 | Episode Length: 10.670 | Episode Return: 0.000 | Time: 0.029\n" ] } ], diff --git a/jumanji/__init__.py b/jumanji/__init__.py index 858d8fe01..4e4c34cef 100644 --- a/jumanji/__init__.py +++ b/jumanji/__init__.py @@ -14,6 +14,7 @@ from jumanji.env import Environment from jumanji.environments.logic.rubiks_cube import generator as rubik_generator +from jumanji.environments.logic.sudoku import generator as sudoku_generator from jumanji.registration import make, register, registered_environments from jumanji.version import __version__ @@ -42,9 +43,20 @@ kwargs={"time_limit": 20, "generator": partly_scrambled_rubiks_cube_generator}, ) -# Sudoku - the standard Sudoku puzzle with grid of size 9x9. +# Sudoku - the standard Sudoku puzzle with grid of size 9x9. By default 10000 puzzles +# of mixed difficulties are set for reset. register(id="Sudoku-v0", entry_point="jumanji.environments:Sudoku") +# Sudoku - the standard Sudoku puzzle with grid of size 9x9. Only 10 very easy puzzles +# are set for reset, they all have 70/81 cells filled. +very_easy_sudoku_generator = sudoku_generator.DatabaseGenerator(level="very-easy") +register( + id="Sudoku-very-easy-v0", + entry_point="jumanji.environments:Sudoku", + kwargs={"generator": very_easy_sudoku_generator}, +) + + ### # Packing Environments ### diff --git a/jumanji/environments/logic/sudoku/data/10000_mixed_puzzles.npy b/jumanji/environments/logic/sudoku/data/10000_mixed_puzzles.npy new file mode 100644 index 0000000000000000000000000000000000000000..e1f09326ce2ea77fc7c7b1d7b267cedb58ce2e6b GIT binary patch literal 810128 zcmb5%%Z?~ZlcwdB=S;5RpA|?o=pMoaFat4RzzCJ-LLi}TRE3xTSHTVQyq`txzoz)! zD>K3m=5{u750A+I_rLw`|C|5re|Y)xfByNu{^39T^S}O2|MCz2)!+W%|NdY9{XhKU z-~Qo${!jn%fBefo{m1|DKmE&p_~+{X>wo&+{`H^L|LgzqPyhQryZ_Js{l8QG@o)d7 z|No1B{M-NYxBvg;Kl}6br}F;x=iRTL&zC>nfBt;Fz5jIg{^#S>@3)Wd_s>6nx~%o% zZNCisdHMPC`cn1BAAsJqzP|kd;`77J`{#$oui92#%wTwczW=KIQpPJuPzq`B{ zzTUb9P`{Ty5c>x6-DqVf8KSyHiZ9Rnz}zP 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