Mountain_Car_Continuous (Fuzzy)/Mountain_Car_Continuous.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2asXWhAGF4p"
      },
      "source": [
        "# Install Requireed Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "SYKmTJ-h-cws"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install gym\n",
        "!pip install scikit-fuzzy\n",
        "!pip install imageio-ffmpeg\n",
        "!pip install pyglet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aASkSzaRFyJN"
      },
      "source": [
        "# Import Requireed Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "wcKzpnsHEGU3"
      },
      "outputs": [],
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "import skfuzzy as fuzz\n",
        "from turtle import delay\n",
        "from skfuzzy import control as ctrl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Define Fuzzy Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\acer\\anaconda3\\lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:122: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
            "  fig.show()\n"
          ]
        },
        {
          "data": {
            "image/png": 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jN2av1rDZ36GxQU5XokDDZlls/G0jr38XO6GxQLQROMAfNnv2881s2nvU6XKco6Exd9KwGb8f/51BCwYx5JIhMRMaC0R/+xxyYUXfzGYfrvJm2MwfGuv6nobG3MbjYbP0jHQeT3mcq8+/OqZCY4FoI3BQ58aVaV2nAoOmr/VW2CwzNPYsnNfQ6WpUTjwcNpu4biLHT8VeaCwQbQQOs8JmJ7wTNvOHxqpfAY1vc7oaFYgHw2aLdi9i1vZZMRkaC0QbgcM8Fzbzh8bav+h0JSoYib2hcjJ8+mDMh812H9vN0MVDYzY0Fog2AhfwTNjsh0UaGos2ItBxtHV6aNlEp6uxjRdCY4FoI3CJmA+baWgsemWGzUbHbNjMC6GxQLQRuEjMhs1OpcGMPtDsbg2NRauyNaDzWCtslrrf6WrCyiuhsUC0EbhIzIbNvh4GRctqaCza1bkWmvSMqbCZl0JjgWgjcJmsYbPN+2IgbLZ+Jmz7UkNjsaL14xCfAP8d4XQlBea10Fgg+pvpQv6wWf8Pojxstn+zhsZiTVw83PQ2bPwYNs11upqQeTE0Fog2ApeK+rDZ8aPW/ecaGos9xcpC18nw2SNRGzbzYmgsEG0ELha1YTMNjcW+yklw5VCY1ivqwmZeDY0Fkq9GICKlRKSkXcWo0/nDZpOjLWy25F8aGvOCpD5WQ4iisJmXQ2OBBNUIRCRZRNYD64ANIrJWRJLsLU2BFTZ7rVtjHp4WJWGzHxZZjUBDY7EvysJmJ9JPMHDBQO5ucLcnQ2OBBHtE8A5wnzGmujGmGnA/8G/7ylJZXXpBlITNNDTmPVEUNvOHxnrW6+l0Ka4TbCM4ZoxZ5H9ijEkBjtlTkspJ/1Y1Obeki8NmGhrzrigIm83ePps1+9d4OjQWSLBXSpaLyETgI8AA3YAFIpIIYIxZndObRKQd8BoQD7xljBmZwzKtgTFAAnDQGNMqf/8J3iAijO7aiM5vLGb26t10SazidEmn+3oYFD074qGx9PR0Dh06xMmTUXybrQ0SEhIoW7Ys8fHxkdmgP2w24w64/ROId89F2I2/bWTMqjFMbj/Z06GxQMQEcZFHROYH+LExxpzxFVBE4oFtQFtgN7AC6G6M2ZRlmTLAEqCdMeYnETnHGBPwK0VycrJZuXJlnjXHqi2/HOW2N5fx4d2XUO+8Uk6XY1k/E/73LPRbEPG8wIEDByhSpAglSpTQb3o+xhhSU1M5fvw4FSpUiNyGM9Lhw5vh3Ivhmmcit90Ajpw4QrfPujEwaaDmBSDXX5Cg2rYxpk0IG20G7DDG7AQQkalAZyDruY3bgNnGmJ9823HncaWL1K1Yiievs8JmcwdcTumiCc4W5A+N9frYkdDYyZMnKV++vDaBLESEEiVKcOxYhM/e+sNmE1tBlaZQv1Nkt59NhsngsUWPcdX5V2kTyEPARiAiPY0xH4jIwJx+box5JcDbKwM/Z3m+G7gk2zJ1gAQRWQCUBF4zxryXZ9Ued0OTyqz+6TCDpq9lUq8k4uIc+hB0SWhMm8CZHNsn/rDZh13hnHpQvrYzdQAT1k7gr5N/aWgsCHldLC7u+7NkLo9AcvqXmP08VCEgCegIXAsME5E6Z6xIpJ+IrBSRlQcOHMhjs94wtGN9fnMybKahMZWbrGGztD8cKWHR7kXM2maFxhLiHD5qjgIBjwiMMRN9f4YywtRuIOs9hFWAvTksc9AY8wfwh4gsBBphXVvIWsckYBJY1whCqCXmFC4Ux7geiXR6YzGNqpTh8toRDsf4Q2M3vR3Z7bpQiRIlSE0NnK59/fXXGT9+PImJidxyyy3UqVOH+vXrR6hCByT1gd0rYO6DcNNbVuYgQvak7mHo4qG80voVKhSL4DWSKBZsoKyCiAwWkUki8o7/kcfbVgC1RaSGiBQGbgWyj1L1CXCFiBQSkWJYp4425/c/wqscC5vtStHQWD6NGzeOL774gg8//JCPP/6YTZtcehtwuIhAx5fh4FZYPilimz2RfoJH5j/CXRffRdK5mnkNVrA5gk+A0sA84PMsj1wZY04BA4AvsT7cpxtjNopIfxHp71tmM/B/WInl5Vi3mG4I5T/Eqy69oDx3Xl6d+yIVNju6D2beZQ0rraGxM4waNYqmTZvSsGFDhg8fDkD//v3ZuXMnnTp14rnnnmPu3Lk8+uijNG7cmO+/j7JxpPIjoSh0fR8WjoKflkVkk88ve57zS51Pr/q9IrK9mGGMyfMBrAlmuUg8kpKSjDpdRkaGufvdFWbonPX2buhUmjFvXWPMgpfs3U4+7Nmzx+kSTPHixY0xxnz55Zemb9++JiMjw6Snp5uOHTuab775xhhjTLVq1cyBAweMMcb07t3bzJgxw/a63LBvjDHGbPmPMS/XM+bYr7ZuZta2Web6Odeb1LRUW7cTxXL9XA029fGZiHQwxnxhY09SIRIRXu7aiE7/SmHOd7u5sYlNYbOvhkGR0nCFe2caq/54wAPVkOwa2TGo5b766iu++uormjRpAkBqairbt2+nZcuWYa8pqlzYDvastGY26/WxLWGzzNBYu8kUTyie9xvUafK6ffQY1p0+AgwWkRPASd9zY4xxSaJJlSqSwPieSfR4axl1K5YKf9hs/UzY+gXc842rZxoL9kPbDsYYnnjiCe655x7HanCt1k9YYbP/PQ1tnw7rqo+cOGLNNNZ8CDXLeHumsVAF/I02xpQ0xpTy/RlnjCma5bk2AZepd14phl1Xj3vDPbPZ/i1WaKzb+9YwEipH1157Le+8807mHUR79uxh//4zM5IlS5aMfNjLaXHx0OUt2DAnrDObZZgMHl/0OFeefyXXVr82bOv1mmDvGrpMRIr7/t5TRF4RkfPtLU2F4sYmVWhZpwL/mBGmmc38obG2T8N5jQq+vhh2zTXXcNttt9GiRQsaNGjAzTffnOMH/q233sqoUaNo0qRJbF8szq54uSwzm+0Iyyonrp3Inyf/5JGkR8KyPq8KdqyhdVj39zcE3gfeBroYBwaI8/pYQ8FIO5VBt0nf0rb+udzX+oLQV2QMTL/dSote/1r4CgyjvXv3UqlSJafLcCXX7puV78DyN+HueVA49PP5KXtSGL54OFOvm6p5geDkGuYI9mTvKWN1jM5Yw0C8Rt7JYuUQf9js34t3sXhHAWY2+/YNOPIztNOZxlQYJd0B5zWGTx8KeWazPal7GJIyhJdavaRNIAyCno9ARJ4AegKf+0YW1dy2i2UNm+07EkLYbNdiWPy6FRpLKBL+ApV3+cNm+7dYRwb55J9pTENj4RNsI+gGnADuMsb8gjWg3CjbqlJhcekF5bnjMitslnYqI/g3Ht1n3ep34wQoo5eClA0KF4Nu78E3L+Y7bPbCsheoWrKqhsbCKM9G4Pv2/4Ex5hXjm6XMGPOT0VFCo8K9rWpRvsRZPPt5kEMapJ+0ZhprehdccJWttSmPK1vTmtlsZvAzm83ZPofV+1frTGNhlmcjMMakA3+KSOkI1KPCzB82W7jtAB9/tyfvN2SGxv5hf3FKXdjOGr125p2Qfirgopt+28Srq15lTOsxGhoLs2BPDR0H1ovI2yLyuv9hZ2EqfPxhs6c/28SWX47mvqA/NNZloqtDYyrGtH4C4hOssFkujpw4wsAFAzU0ZpNgf9s/B4YBC4FVWR4qSvjDZv3fX8XR4zmEzTQ0Zqu5c+cycuQZU3YD1jDWOenTpw8zZ84EoHXr1sTsbdOZYbPZsPnTM36cYTJ4YtETGhqzUbBTVb4rIkWB840xW22uSdnkxiZV+O6n3xk0fS0Te2aZ2UxDY7br1KkTnTo5O3WjqxUvB13ftWY2q1APyv+df5m4biJ/nPxDQ2M2CjZZfD2wBmvIaESksYiELyeuImZox/ocTD3BhIW+RKsxMHcAVLsUmvR0trgotWvXLurWrcvdd9/NxRdfTI8ePZg3bx6XXXYZtWvXZvny5UyePJkBAwYA8MMPP9CiRQuaNm3KsGHDMtdjjGHAgAHUr1+fjh075jg8BViD27Vo0SJzkpu8JsWJGpWT4Moh1pcS38xmKXtSmLl1ps40ZrNgTw09hTUZ/e8Axpg1QA1bKlK2OiNs9u0b8PtP0P4lp0uLajt27OChhx5i3bp1bNmyhSlTppCSksLo0aN5/vnnT1v2oYce4t5772XFihVUrFgx8/U5c+awdetW1q9fz5tvvsmSJUvO2M7Bgwd59tlnmTdvHqtXryY5OZlXXgk0dXiUSboDKjWBTx9iz7HdGhqLkGDHgz1ljDmS7XYtnTIySvnDZpM/+pDmZ/2L+H7/i53Q2FM23Nz21JE8F6lRowYNGjQA4KKLLuKqq65CRGjQoAG7du06bdnFixcza9YsAHr16sVjjz0GwMKFC+nevTvx8fFUqlSJK6+88oztLF26lE2bNnHZZZcBkJaWRosWLQryX+cuvrDZibfbMvCL3hoai5BgG8EGEbkNiBeR2sCDwJlfV1TUuPSckzSM/xfPJDzE4BJVKOx0QeESxIe2Hc466+8pO+Pi4jKfx8XFcerUmbdF5nYPfF73xhtjaNu2LR999FEBqnW5wsV4oe4lVN32H3qVvNDpajwh2FNDDwAXYaWLPwKOAg/bVJOymy80VvzSvuwp1yL4sJkKi8suu4ypU6cC8OGHH2a+3rJlS6ZOnUp6ejr79u1j/vz5Z7y3efPmLF68mB07rNE7//zzT7Zt2xaZwiNkzvY5rD6ygxFXvIDMvANSDzhdUswLqhEYY/40xgwBrgLaGGOGGGOO21uass3XT0KR0kjLRzPDZnO+2+10VZ7x2muvMXbsWJo2bcqRI38fwdx4443Url2bBg0acO+999Kq1ZmD+1aoUIHJkyfTvXt3GjZsSPPmzdmyZUsky7fVaaGx+jdAo+5W8jiPsJkqmGCHoW4KvMPfI44eAe40xkQ8S6DDUBfQhtkw7ylrpjFfXmDLL0e57c1lTOl7CXUrRtd8Q64datkFom3fHDlxhG6fdePhpIdpV72d9WJGOnxwk3Vbc9sRzhYY/Qo8DPXbwH3GmOrGmOrA/cC/w1CYiqT9W+CLf5wRGqtbMY+wmVI2yxoay2wCYIXNbnobNszKMWymwiPoYaj9A84BGGNSAI/NtRfl8giN3dikClfUrsCg6WGa2UypfAgYGiteDm55Fz59OGwzm6nTBWwEIpIoIonAchGZKCKtRaSViIwDFkSkQlVwxsAn90P1ywKGxoZeV48Dx7KEzZSKgKBCY1V8YbPpvTLDZip88rp99OVsz4dn+bt+bYwW/pnGugSeBOSsQvGM65FI57GLaVSlDJddUD5CBSqv2pO6h6EpQ3m59ct5h8aS7oCfV1gzm3V508ocqLAIeERgjGkT4HFm2kW5Tz5nGqtUpihjCjKzmVJB8s80dsfFdwQXGivgzGYqd0EFykSkDHA7UD3re4wxD9pSlQqPo/tg1l35nmnssgvK0+dSa2azaf1aULiQDkmtwu+FZS9QuURlbq9/e/Bv8s9s9vY1UKkxVG1mW31eEuxv+BdYTWA9Ogx1dPDPNJZ8Z0gzjd3bqhbliudjZjOVqUOHDvz+++8Bl8ltWOk1a9bwxRdf2FSZe/hnGnvmsmfyP9NY2ZrQ6Q3r37eGzcIi2EZQxBgz0Bjzb2PMu/6HrZWpgvGFxkKdaSwuLp8zmynAGgLis88+o0yZMiG93wuNYPNvmws+09iF7TRsFkbBNoL3RaSviJwnImX9D1srU6HbMBu2fF7gmcZKFw1yZjOP27VrF/Xq1eO+++4jMTGR+Ph4Dh48CMAzzzxD3bp1adu2Ld27d2f06NGZ75sxYwbNmjWjTp06LFq0iLS0NJ588kmmTZtG48aNmTZtmlP/SbY5cuIIjyx4hMHNBxd8prE2gyGuEPzvmfAU52HBfkqkAaOAb/n7tJDGe90ol9BYqOqdV4qhHTVslpetW7dy++23891331GtWjUAVq5cyaxZs/juu++YPXv2GaeCTp06xfLlyxkzZgwjRoygcOHCPP3003Tr1o01a9bQrVs3J/5TbJNhMhicMpg2VducHhoL1Wlhs88Kvj4PC3b00YHABcaYg3YWowroxDHrPuswzzTWJbEKq386zD+mr2Vir6T8n9ONoAbvNgj7Otf3Xp/nMtWqVaN58+anvZaSkkLnzp0pWrQoANdff/1pP+/SpQsASUlJZwxVHYsmrZvEsbRjDEweGL6V+sNmU7rCOfWgXK3wrdtDgm0EG4E/7SxEFZA/NHZ+c1tmGht2XX26TlzKhG92cm9r9/6yBfOhbYfixc88153XOF7+oarj4+NzHKo6lqTsSWHG1hlMvW5q+Gcaq5JknSaa1hPungeFQ7zu4GHBnhpKB9b40sWv+x92Fqby6duxcPhHaD/KltWfVSie8T0SeTvlB5bs0APDYFx++eV8+umnHD9+nNTUVD7//PM831OyZEmOHYut0Vv2pO5hSMoQXmz5on0zjSXfaR0Ff/qw9aVI5UuwjeBj4DmsyWj09lG32bUYFo8JOjQWKn/Y7CENmwWladOmdOrUiUaNGtGlSxeSk5MpXTrwDGpt2rRh06ZNMXOx2B8au/PiO0mumGzfhkSg4yuwfxOseMu+7cQqY0xQD6AocGGwy/ve0w7YCuwAHg+wXFOso46b81pnUlKSUVkc3WfM6AuN2fZ1xDb5xv+2mxvGppgTJ9Mjts3c7Nmzx+kSAjp27Jgxxpg//vjDJCUlmVWrVkVs227YN8MXDzePzH/EZGRkRGaDv31vzIs1jflpeWS2F11y/VwN6ohARK4H1gD/53veWETm5vGeeGAs0B6oD3QXkfq5LPci8GUwtags0k/CjDsgqQ/Uvjpim7XCZoV5TsNmeerXrx+NGzcmMTGRm266icTERKdLipg52+ew6tdVoYXGQlW2JnR+A2b01rBZPgR7sfgpoBm+EUeNMWtEpEYe72kG7DDG7AQQkalAZyD7p8cDwCysowKVH18Pty6MtfxnRDdrhc0a0+mNFD7+bg83NKkc0e1HkylTpjhdgiP8obF/t/t36KGxUF3YHnavgFl3Qs85EB/sx5x3BXuN4JQxJvus4HldkakM/Jzl+W7fa5lEpDJwIzAhyDqU34bZsOVT6DKpQKGxUJUumsD4HlbYbOsvzl7cNHpx8AxO7pPM0Nglg6lVxqE7zNoMAYmD+c86s/0oE+wnyAYRuQ2IF5HaIvIvrAvHgeR0LJj9X+cY4DFjTHrAFYn0E5GVIrLywAE93OPAVis01vV9KOZcwLt+JV/Y7APnwmYJCQmkpqZqM8jCGENqaioJCWG+TTMI/pnG2lRtQ7saYQiNhcofNls/00rZq4CCPWZ6ABgCnAA+wjqfn1euezdQNcvzKsDebMskA1N95w/LAx1E5JQx5uOsCxljJgGTwJqzOMiaY9OJY9b90lePsEZfdJg/bPbojLVM6Bn5sFnZsmU5dOhQzN1yWVAJCQmULRv5LwmT1k3ij5N/hDc0Fqri5eGWyTClG1Soq2GzAIKavD6kFYsUArYBVwF7gBXAbcaYjbksPxn4zBgzM9B6PT15vTHWiItFSkGnfzldTaYTp9LpOnEp7S+uSP9W+svmVUv2LGHY4mFMvW6qfXmBUKx4C1a8A3d/7fWwWa7f0gIeEeR1Z5AxplOAn50SkQFYRw/xwDvGmI0i0t/3c70ukF9Lx8HhXXCnu26w8ofNOo9dTMMqpbm0ls5s5jV7U/cyOGUwo1qNclcTAEi+y5rZ7LNH4MaJOrNZDgIeEYjIAawLvh8By8jWUYwx39haXQ48e0Tw4xKYfjvc/V84u5rT1eQoZftBBk5fw9wBl1OxtH3BNuUuJ9JP0Ps/vWlfoz29L+rtdDk5S/sT3m5r3WrdrK/T1Tgl1w6Y18XiisBg4GLgNaAtcNAY840TTcCzjv0CM++EGya4tgkAXF67PL0vrc59H64i7VSG0+WoCBm5fCSVSlTK30xjkVa4mJW8XzDSOjpQp8lrzuJ0Y8z/GWN6A82xEsILROSBiFSnHAuNhereVrUoW7wwz3+x2elSVAR8vONjVv6yMrKhsVCVq2VdW5vRB/7Q8bKyyvP2URE5S0S6AB8A9wOvA7PtLkz5zHvKkdBYqPxhs/lb9/PJGp3ZLJZtObSFV1a+wpg2BZhpLNLqdoCGXXVms2wCNgIReRcrL5AIjDDGNDXGPGOM0d/wSNg4BzbPdSw0Fip/2GzEp86HzZQ9jpw4wiPzHQ6NherKoRo2yyavT5deQB3gIWCJiBz1PY6JiM5daKcDW+HzQY6HxkLlhrCZsoc/NNa6amtnQ2Oh0rDZGfK6RhBnjCnpe5TK8ihpjCkVqSI9x2WhsVB1SazCZReU49EZazX5G0NcFRoLlT9sNvdB+O17p6txXPScb/AKY+CTAdZMY4m9nK6mwIZdV59fjp5g4sKdTpeiwmDxnsXM2DqD0a1Gh3+msUirkgxtnoBpvSDtD6ercZQ2Arfxh8Zsmmks0k6b2ex7vVMjmu1N3Wv/TGORlnwXVGxghc08fNSqjcBNflwCKa/aPtNYpFUqU5RXuzbm4alr+OXIcafLUSHwzzR2x8V32DvTWKSJwHWvwq8bPT2zmTYCt4iS0FioNGwW3aIiNBYqDZtpI3CFKAuNhUrDZtEpqkJjofJ42EwbgRtEWWgsVBo2iz5RGRoLlYfDZtoInBalobFQadgsekR1aCxUHg2bxf4nj5tFeWgsVPUrlWJIBw2buVmGyWBwyuDoDY2FyqNhM20ETjlxzLp/OcpDY6G6KakKl9bSsJlbvbnuTY6lHYvu0FioipeHW971VNhMG4ETMkNjl8REaCxUT16vYTM3WrJnCdO3To+N0FioqiR5KmymjcAJMRYaC9VZheIZ1yORtxZp2Mwt/DONjWw5knOKneN0Oc7yUNhMG0GkxWhoLFSVyxTl1W6NeEjDZo5LS09j0IJB9LmoD00rNnW6HOd5KGymjSCSYjw0Fqoralegd4tqGjZz2MjlIzmvxHnunW7SCR4Jm2kjiBSPhMZCdV/rCzRs5qBPdnzCil9W8PSlT8duaCxUHgibaSOIlHlPWd8uYjw0Fqq4OOHlWzRs5oQth7bw8sqXebX1q5QoXMLpctwpM2x2J2SkO11N2GkjiITM0NibngiNhap0sQTG9UjUsFkE+UNjT1zyBBecfYHT5bjblUOtP/8Xe2Ez/VSyW2Zo7D1PhcZCdVGl0gzuUI97P1jFMQ2b2SrDZDAkZQitqraifY32TpfjfnHxcPM7sG56zIXNtBHY6USqdR/yVcOhUhOnq4kaNydVoXmtcjw6Y52GzWz01vq3OHLiCIOSBjldSvQoXh66xl7YTBuBXYyBuQOgalNI0rsw8mv49fXZd+QvJmnYzBZL9ixh6papVmgs3qOhsVBVSYbWj8P02yHtT6erCQttBHZZOt76xtBhtNOVRKWzCsUzrmcSby76gW+//83pcmKKPzT2YssXObf4uU6XE52a3g3nXhwzYTNtBHb48VtIeQW6vQ8JRZ2uJmpVLlOUV7o24qGp32nYLEz8obHeF/XW0FhB+MNmv6yHlW87XU2BaSMIt2O/WreYdR4HZ1d3upqo17JOBXo1r8b9U1Zr2CwMRi4fScXiFelzUR+nS4l+hYtZX/bmvwC7VzpdTYFoIwin9JPWpBaJvaDONU5XEzPub3MBZYomaNisgPyhsZieaSzSytWCTq9HfdhMG0E4zXvKOhXU6jGnK4kpcXHCK900bFYQGhqzUd2O0OBmmHVX1IbNtBGEy8aPs4TG4p2uJuaULvp32Gzbrxo2y4+jaUc1NGa3NkPBZMD855yuJCTaCMLhwDb4fKCGxmzmD5v1f1/DZsHKMBkMXjRYQ2N2iy8EN70Da6fBli+cribftBEU1IlUmNZTQ2MRomGz/NHQWASVqAC3TIa5D0Rd2EwbQUEYY/1P19BYRPnDZm8u0rBZIEv2amgs4qo2jcqwma2NQETaichWEdkhIo/n8PMeIrLO91giIo3srCfslk2A33ZoaCzC/GGzSQt/YOlODZvlZF/qPgYv0tCYI5reDefUt04XR8lRq22NQETigbFAe6A+0F1E6mdb7AeglTGmIfAMMMmuesLup6Ww6GUNjTnEHzZ78KPv+PWohs2ySktPY+CCgRoac4oIXD8G9q2Fle84XU1Q7DwiaAbsMMbsNMakAVOBzlkXMMYsMcYc9j1dClSxsZ7wOfarNcmMhsYc5Q+b3fehhs2yenH5ixoac1rh4tDtA5j/fFSEzexsBJWBn7M83+17LTd3Af+xsZ7w0NCYq2jY7HSf7PiE5b8s19CYG0RR2MzORpDTv8IcT5iJSBusRpBjEktE+onIShFZeeDAgTCWGAINjbmKhs3+pqExF4qSsJmdjWA3UDXL8yrA3uwLiUhD4C2gszEmxyt/xphJxphkY0xyhQoVbCk2KBoacyUNm+lMY64WBWEzOxvBCqC2iNQQkcLArcDcrAuIyPnAbKCXMWabjbUUnIbGXM3LYTOdaczloiBsZlsjMMacAgYAXwKbgenGmI0i0l9E+vsWexIoB4wTkTUi4s6rKhoaiwpeDZtpaCwKuDxsJtH2C5OcnGxWroxgvzDGGla6cDHoPDZy21UhOXEqna4TvqVjw/Po17KW0+XYbsneJQxNGcpHHT/SvEA0WP4mrJoMd31tfaZEVq53D2iyOC8aGosqXgqbaWgsCrk0bKaNIBANjUUlL4TNNDQWpVwaNtNGkBsNjUW1zJnNPlzNyfTYC5tpaCyKuTBspo0gJxoaiwn3t7mA0jEYNpv7/VwNjUU7l4XNtBHkZN5TUKiIhsaiXFyc8ErXxvx3837mrj0jwhKVth7ayugVozU0FgtcFDbTRpCdPzR201saGosBpYslML5nIk/N3cj2KA+bHU07yiMLHuHxZo9raCxWuCRspo0gKw2NxaSLKpXmifZ1ueeD6A2bZZgMhiwawhWVr6BDzQ5Ol6PCxSVhM20Efhoai2m3JFflkhrl+OfM6Aybvb3+bQ6fOMw/kv/hdCkq3FwQNtNGAH/PNFalKSTe7nQ1yibDr6/Pnt//4q1FPzhdSr58u/dbPtryES+3ellnGotVDs9spo0A/g6NdRxt3eerYlKRhHjG9Uhk4sLvoyZs9ssfv/DEoicYecVIDY3FOgfDZtoI/KGxru9paMwDqpxdjJe7Nuahqe4Pm/lDY73q96LZec2cLkfZzcGwmbcbQWZobCyUreF0NSpCWtWpQI9L3B82e2nFS5xT7BzuvPhOp0tRkXJa2GxVxDbr3UaQfsoaTK5JT6hzrdPVqAgb0OYCShVN4IUvtjhdSo4+/f5Tlu5bqqExLypXC65/DWb0hj8icwrTu43gv09BocLWBRrlOXFxwqtdG/P15l/4bJ27wmZbD21l1IpRvNr6VUoWLul0OcoJ9a6Di2+CWXdGJGzmzUaw6RPY+Anc9LaGxjysdLEExvdI4slP3BM284fGHmv2GLXPru10OcpJVw6zmsD8523flPcawcHt8Nkj0HWyhsYUF1cuzeMuCZv5Q2OXV76cjjU7OlqLcoH4QnDzv2HtR7D1P7ZuyluNwB8au3IYVE5yuhrlEl2Tq3JJjbKOh838obFHkx91rAblMv6w2ScD4NBO2zbjnUZgDHz6oNUAkvo4XY1ymeHXX8Tuw86Fzb7d+y1TtkxhdKvRGhpTp6vazBoAc5p9YTPvNIJlE+HgNuj4sobG1BmKJMQzvmciExfuZFmEw2ZZQ2MVi1eM6LZVlGjWF86pC58PsiVs5o1G8NNSWDgKuupMYyp3VtisEQ9GMGyWNTR2yXmXRGSbKgqJWLeU7lsDq/4d9tXHfiNI3W+Fxm4Yp6ExladIh800NKaCVri49WX2f8/BnvCGzWK7EWhoTIUgUmEzDY2pfCt/gTUMxfTwhs1iuxH8dwTEJ2hoTOVLJMJmGhpTIat3PVzcJawzm8VuI9g015ptTENjKgR2hs00NKYK7MonIeMULHghLKuLzUagoTEVBv6wWf8PVpF64lRY1plhMhiSoqExVUDxheDmd2DNFNj6fwVeXew1grQ/YFovuHKohsZUgXVNrkqzGmX558y1YQmbvbPhHQ4f19CYCoMS51jJ40/uh0MFy7/EViMwBuY+CJUTNTSmwmb49Rfx86G/eDulYL9sS/ctZcpmDY2pMDr/Emj1T+vL78m/Ql5NbDWC5ZPg4FYNjamw8ofNJnwTetjMHxp74YoXNDSmwqtZPyts9lnoM5vFTiP4aZmGxpRtsobN9uczbJaWnsagBYPoWa+nhsZU+IUhbBYbjSB1P8zoA53e0NCYsk1m2GxK/sJmL614iQrFKmhoTNmngGGz6G8EmaGxHnBhO6erUTFuQJsLKFkkgZH/CS5spqExFTEFCJtFfyPIDI094XQlygMyw2abfuXTtYHDZhoaUxEXYtgsuhvBprmwcQ50eUtDYypiShdLYHzPREZ8uonnPt/En2mnZwwyTAZTNk+h71d9Gdx8sIbGVGSFEDaL3kZwcDt89jB0fReKl3O6GuUxF1UqzZcPX8GBYydoN2YRKdsPAvD9799z+39u58tdXzK5/WTaVdfTlSrCQgibiZ0zMolIO+A1IB54yxgzMtvPxffzDsCfQB9jzOpA60xOTjYrl3wDb14Fl/SDZL0Ap5w1f+t+hsxZw7nnL+GA/JcHmgzglgtvIU6i93uWigE/LYOpt8Hd8/w30eR6kcq2f6kiEg+MBdoD9YHuIlI/22Ltgdq+Rz9gfFArn/sgVGoCSXeEr2ClQlS27D4q1B1HKj9w/McHKZHWEsn9d06pyPCHzabnHTaz8ytLM2CHMWanMSYNmAp0zrZMZ+A9Y1kKlBGR8wKu9Y8DcEBDY8p5f578k5HLR/Lw/Ie5t1F/vuo+mYndr+bVedvo+94qfjkSmcltlMpVs35Q/kIrbBZAIRtLqAz8nOX5biB7mianZSoD+3Jb6U9/7mdAjatg0T/DVadSIdl6eCvNKjZjTqc5lClSBoCkamfz+YOXM27+93R4fRGNq5bRYwPlqMIZvRmy7wGq3Jj7MnY2gpz+/We/IBHMMohIP6xTRwAnxrb/94YC1hYp5YGDThcRJK01BPOYx/M8n+vPv3NRrUHQWu3heK0TAIbK/xljcrx7wc5GsBuomuV5FSD7jdfBLIMxZhIwCUBEVhpjksNbqj20VntorfbQWu0RDbXaeY1gBVBbRGqISGHgVmButmXmAreLpTlwxBiT62khpZRS4WfbEYEx5pSIDAC+xLp99B1jzEYR6e/7+QTgC6xbR3dg3T6qtwEppVSE2XlqCGPMF1gf9llfm5Dl7wa4P5+rnRSG0iJFa7WH1moPrdUerq/V1kCZUkop99Poo1JKeVxUNQIRaSciW0Vkh4g87nQ9gYjILhFZLyJrRGSl0/VkJSLviMh+EdmQ5bWyIvK1iGz3/Xm2kzX65VLrUyKyx7dv14hIBydr9NVUVUTmi8hmEdkoIg/5Xnfdfg1Qqxv3axERWS4ia321jvC97sb9mlutrtuv2UXNqSHfkBXbgLZYt52uALobYzY5WlguRGQXkGyMcd29ziLSEkjFSnVf7HvtJeCQMWakr8mebYx5zMk6fXXlVOtTQKoxZrSTtWXlS8SfZ4xZLSIlgVXADUAfXLZfA9TaFfftVwGKG2NSRSQBSAEeArrgvv2aW63tcNl+zS6ajgiCGbJCBcEYsxA4lO3lzsC7vr+/i/XB4LhcanUdY8w+/4CJxphjwGaslLzr9muAWl3HN/xMqu9pgu9hcOd+za1W14umRpDbcBRuZYCvRGSVLxntduf6Mxy+P89xuJ68DBCRdb5TR46fFshKRKoDTYBluHy/ZqsVXLhfRSReRNYA+4GvjTGu3a+51Aou3K9ZRVMjCGo4Che5zBiTiDXC6v2+UxwqPMYDtYDGWONSvexoNVmISAlgFvCwMeao0/UEkkOtrtyvxph0Y0xjrJEHmonIxQ6XlKtcanXlfs0qmhpBUMNRuIUxZq/vz/3AHKxTW272q3/kV9+f+x2uJ1fGmF99v3AZwJu4ZN/6zgvPAj40xsz2vezK/ZpTrW7dr37GmN+BBVjn3F25X/2y1ur2/QrR1QiCGbLCFUSkuO8iHCJSHLgGcPtAeXOB3r6/9wY+cbCWgOT0ocpvxAX71neh8G1gszHmlSw/ct1+za1Wl+7XCiJSxvf3osDVwBbcuV9zrNWN+zW7qLlrCMB329UY/h6y4jlnK8qZiNTEOgoAK709xU21ishHQGusURF/BYYDHwPTgfOBn4BbjDGOX6TNpdbWWIfZBtgF3OP0GFUicjmwCFgPZPheHox17t1V+zVArd1x335tiHUxOB7ri+t0Y8zTIlIO9+3X3Gp9H5ft1+yiqhEopZQKv2g6NaSUUsoG2giUUsrjtBEopZTHaSNQSimP00aglFIep41AKaU8ThuBUkp5nDYCpQpIRJr6BhQr4kuVb3TzeDhKZaeBMqXCQESeBYoARYHdxpgXHC5JqaBpI1AqDHzjX60AjgOXGmPSHS5JqaDpqSGlwqMsUAIoiXVkoFTU0CMCpcJAROZizZpXA2sayAEOl6RU0Ao5XYBS0U5EbgdOGWOm+ObWXiIiVxpj/ud0bUoFQ48IlFLK4/QagVJKeZw2AqWU8jhtBEop5XHaCJRSyuO0ESillMdpI1BKKY/TRqCUUh6njUAppTzu/wEL9P2gWj609wAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = ctrl.Antecedent(np.arange(0, 40, 1), 'x')\n",
        "x.automf(3, names=['left', 'middle', 'right'])\n",
        "x.view()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\acer\\anaconda3\\lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:122: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
            "  fig.show()\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "v = ctrl.Antecedent(np.arange(0, 40, 1), 'v')\n",
        "v.automf(3, names=['slow', 'middle', 'fast'])\n",
        "v.view()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\acer\\anaconda3\\lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:122: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
            "  fig.show()\n"
          ]
        },
        {
          "data": {
            "image/png": 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FA77QlPKk9brS18OcnsQxx88c59FFj0ZtwOeNfqc6JOrDPw34QldW+DfPeosyGZkZPJX6FNdfen3UBnze6KLhoKgN/zTgC31FS1tHsi0YCEd2OD1NUE3cOJEzF6I74PNGFw2HRV34pwFf+KiSCG2HRlX4tzR9KbN3zI76gM8bXTQc5hn+pe444vQ49tOAL7wk9oIqzeDThyM+/Es/lc6QZUM04MuDLhohoFJ8EV7v1oRHpq9nXySHfz8u1YAv3LjDvyM7YNVEp6exzdmMswxcNJB7G96rAV8edNEIEdfUKss91yXwwNR1kRn+ndwHs++FLroDX9jJCv9GRmz45w74etTt4fQoIU8XjRDSL6UGFUoU4oUFERb+XTgHM3tDi3uhZhunp1H+KJ0AncZa4d/pQ05PE1Czv5/N+kPrNeDzkS4aIUREGNm1Mct2/sKcdREU/n05FIqUhusedXoSlR+X3wRNe0RU+OcO+Ea3Hq0Bn4900Qgx7vDvxc/SSNsfAeHfplnw/Rca8EWKVk9BbBx8/ZzTk+SbBnz+0e/iEOQO//p9GObh36E0K+Dr+r4GfJEiJhZum2zt9rd1vtPT+E0DPv/pohGi/gj/NoRn+HfmpHV8/40vQqVGTk+jAqloaej6LiwYELbhnwZ8/tNFI4RZ4d/Z8Av/3AFf9ZbQ5E6np1F2qJIEbYbA9J5w7lenp7koGvDlz0UtGiJSUkRK2DWM+jN3+Pfe8t0s2xlG4d/yN+HEXmj/itOTKDsl9baq8fnhE/5pwJd/Pi0aItJMRDYBG4HNIrJBRJLsHU2BFf6NCafwb3eqtWh0fV8DvkgnAjePgiPbYfUkp6fJkwZ8geHrI40pwIPGmOrGmMuA/sB/7BtLebqmVln6XJvAg6Ee/p3cB7PusY6U0oAvOsQVga4fwJJX4edVTk/jlQZ8geHronHKGLPU/Y4xJhU4Zc9IKif9UmpQPpTDP8+Ar1Zbp6dRwZQV/vUO2fBvzo45GvAFiK+vAq0WkYnAx4ABugGLRCQRwBizLqdPEpF2wOtALPCOMeYvZ6kTkVbAGCAOOGKMSbm4/4Xo4A7/Or21jDnr0umSWNXpkf4siAFfRkYGR48e5fz5MD4cOYji4uIoXbo0sbGx9n0Rz/Cv5ycQGzovMG/5ZQtjvh3Du+3f1YAvAMT48AKWiHzj5WJjjPnLuSFEJBb4HrgBSAfWAHcYY7Z6XKcUsBxoZ4z5WUTKG2O8/qrSrFkzs3bt2jxnjlTbDpzkzrdXMfXeK6lbqaTT41g2zYL/exH6LgpKj3H48GEKFy5M8eLF9bfGPBhjOH36NGfOnKFcuXL2frHMDJj6d6jQAG58wd6v5aPjZ45z+2e3MzBpoPYYEJBvFp9+HTDGtPbjtlsAO40xuwBEZBrQCfB8fuVOYI4x5mfX1wnNx7YhpE7Fklnh3/yHriO+SJyzA7kDvp6fBC3gO3/+PGXLltUFwwciQvHixTl1KgjPJrvDv4kpULU51LvV/q/phQZ89vC6aIhID2PMhyIyMKfLjTGvefn0KsAej/fTgSuzXedyIE5EFgElgNeNMe/nOXWU69SkCut+OsajMzYwqWcSMTEO/fA8c9I6Tt+BgE8XDN8F9b5yh39Tu0L5ulC2dvC+djYa8NkjrxfCi7n+WyKXN29y+pea/bmwAkAScDNwEzBURC7/yw2J9BWRtSKy9vDhw3l82ejgePiXFfBdpwGf+rMQCP804LOP13vTGDPR9V9/zk6WDnged1kV2JfDdY4YY34FfhWRJUBjrNdCPOeYBEwC6zUNP2aJOO7wr9Nby2hSrRTX1gpyqOQO+G6bHNyvGyKKFy/O6dPRsQWqX5J6Q/oaK/y77R2r6QgSd8A3utVoDfhs4GvcV05EBonIJBGZ4n7L49PWALVFJEFECgK3A9nPcDYPaCkiBUSkKNbTV2kX+z8RrRwL/zTgU3lxKPzTgM9+vnYa84B44CvgM4+3XBljLgAPAV9gLQQzjDFbRKSfiPRzXScN+C9Wab4a67Dczf78j0SroId/J/drwOfBGMPjjz9OgwYNaNiwIdOnTwfgwQcfZP5863ekzp0706dPHwAmT57MkCFDHJs3qBwI/zTgs5+vT/YVNcY8ebE3boxZCCzM9rEJ2d5/FXj1Ym9b/aFfSg2++/kYLy5I44W/NbDvC2WctwKu5hrwuc2ZM4f169ezYcMGjhw5QvPmzUlOTiY5OZmlS5dy6623snfvXvbv3w9Aamoqt99+u8NTB1HpBLj1LZh1t3VIdvHytn0pd8D38c0f64ESNvJ10VggIh1ci4AKMZ7h39zv0unc1Kbw739DrcNqW4bWDnzVn/L6oNcvu4ff7NP1UlNTueOOO4iNjaVChQqkpKSwZs0aWrZsyZgxY9i6dSv16tXj2LFj7N+/nxUrVvDGG28EfN6QdkU72LvW1vBPA77gyeuQ21NYRzwJMEhEzgLnXe8bY0yI1GWqZOE4xvdI5M63V1GnYsnAh3+bZsH3n1u/LYbYDny+/oC3Q25xbJUqVTh27Bj//e9/SU5O5ujRo8yYMYPixYtTokQUnii61dNW+Pd/z8MNzwf0pk+cPaE78AWR1+9+Y0wJY0xJ139jjDFFPN7XBSPE1KlYkmc61uOBQO/4l7UD3wdQ5JLA3W4ESE5OZvr06WRkZHD48GGWLFlCixYtALj66qsZM2YMycnJtGzZkpEjR9KyZUuHJ3ZITCx0eQc2zwnojn+ZJpOnlj5F20vbasAXJL4ePXWtiBRz/bmHiLwmIpfaO5ryx9+aViH58nI8NjNAO/65d+C74QXdgS8HnTt3plGjRjRu3Jg2bdowYsQIKlasCEDLli25cOECtWrVIjExkaNHj0bvogFQrAx0fQ8WPBKwHf8mbpjIb+d/04AviHw999RGrH6iEfABMBno4sTJBaP93FO+OHchk26TVnBDvQo82KqW/zdkDMzoCUXLwC2vB27AfNq3bx+VK1d2eoywElL32dopsGoS3Pc1FCyW9/Vzkbo3lWeXPcu0jtMoV9Tm82pFhoAcHeDrk9MXjLW6dMI61cfr5F2EK4cULBDDuO6J/GdZPnf8W/4mnEiHdroDnwqgpLuhctN87fi39/ReBqcOZkTKCF0wgszn/TRE5GmgB/CZ6wy2Dp8pT3lTKb4Ir7vCv/0n/Aj/PAO+uMKBH1BFL3f4d9i/8M8d8N3T4B6SKugGosHm66LRDTgL3GOMOYB1MkJtK0LcNbXKcve11Xlw6jrOXcj0/RP/FPDpS1fKBgWLQrf3YfGIiw7/Xl71MtVKVKNnvZ42Dae8yXPRcD2q+NAY85p79z5jzM96Ntrw8EBKTcoWL8SLn/m4458GfCpYStewdvybdbfPO/7N3TGXdYfW6Q58Dspz0TDGZAC/iUh8EOZRASYijOramCXfH2bud+l5f0KIBnwqQl3RzjpL8qw+kHHB61W3/rKV0d+OZkyrMRSL8/8FdJU/vj49dQbYJCKTReQN95udg6nAscK/JF5YkEba/pO5X9Ed8HWeEHIBn4pgrZ6G2Dgr/MvFibMnGLhoIIOvGkyNUhrwOcnXnwyfAUOBJcC3Hm8qTNStVJKhHevmHv4d2qYBn0NatWpFXoeRT5gwgfffj9BnhD3Dv7RP/3KxO+Brc2kbbqp+kwMDKk++bvf6nogUAS41xmy3eSZlk85Nq7Lup+M8NnMDE3t47PinAV/I69evn9Mj2Msd/k3tCuXqQtk/+iJ3wDcgaYCDAyo3X4vwW4D1WKcxR0SaiEjgzgWggmZIx7ocPnWWCUtcO/5l7cB3LTTt7uxwYWT37t3UqVOHe++9lwYNGtC9e3e++uorrr32WmrXrs3q1av59ddf6dOnD82bN6dp06bMmzcPgN9//53bb7+dRo0a0a1bN37//Y9DoosXL57151mzZtG7d28Ahg0bxsiRIwHrkcmAAQNITk6mbt26rFmzhi5dulC7du3wPu16lSRoM9j6Bca141/q3lRmfT+LkSkjiYvRo/xDga+nmxwGtAAWARhj1otIgk0zKRsVKhDLuO6JdBq7jMZVS3HtwY+sgK/LO06PFnZ27tzJzJkzmTRpEs2bN+ejjz4iNTWV+fPn89JLL1GvXj3atGnDlClTOH78OC1atOD6669n4sSJFC1alI0bN7Jx40YSEy9+s6CCBQuyZMkSXn/9dTp16sS3335L6dKlqVmzJgMGDKBMmTI2/B8HQdLdsMfa8W/vTc8xOHUwr7V6TQO+EOLronHBGHMi2yFuuu1qmKpcytrx7/2Pp3JVoTeJ7ft/4R3wDbPhwL5hJ/K8SkJCAg0bNgSgfv36tG3bFhGhYcOG7N69m/T0dObPn5/1COHMmTP8/PPPLFmyhIcffhiARo0a0ajRxT8leOuttwLQsGFD6tevT6VKlQCoUaMGe/bsCd9FwxX+nZ18AwMX3kWfBn004Asxvi4am0XkTiBWRGoDDwPL7RtL2e3a8udpEvsGL8b9i6eLV6Wg0wPlhw8/4O1QqNAfW93GxMRkvR8TE8OFCxeIjY1l9uzZXHHFFX/53NwaA8+PnzlzJs+v7fl1Pb92WCtYlJfrXEmV7z/nruJ/ve+Us3w9euqfQH2sKvxj4CTwiE0zKbu5Ar4i1/RlT+lrfA//1EW56aabePPNN7P23Pjuu+8A63TqU6dOBWDz5s1s3Lgx63MqVKhAWloamZmZzJ07N/hDh4C5O+ay7sROXmj5MjK7j8/hnwoOnxYNY8xvxpjBQFugtTFmsDEm91+DVGj731AoHE9M8uNZ4d8n3+11eqqIM3ToUM6fP0+jRo1o0KABQ4cOBeCBBx7g9OnTNGrUiBEjRmTtvwEwfPhwOnbsSJs2bbKecoomfwr46v0NGt/hU/ingsfXU6M3B6bwx5ltTwB9jDFBbzX01Oj5tGkWfP083L84q8dI23+S7u+s4qP7rqROxdDfWyukTvMdJsLhPjtx9gTdFnTjkaRHaFe9nfXBzAz48DbrUPAA7/gXhYJ6avTJwIPGmOrGmOpAf+A/gRhABZE74Ov254CvbqWSDLm5Lg98uI6TZwK4459SPso0mTy99GlaV2v9x4IBVvh32+Rcwz8VfD6fGt19skIAY0wqcMqekZQtsgK+56FS479c3CWxKtfVKstjMzbkuu+1UnaZuHEiv57/lYHNBv71wmJl4B/vwaePwJGdQZ9N/ZnXRUNEEkUkEVgtIhNFpJWIpIjIOFzNhgoDxsC8/nDZNdC0R65XG9KxLodOnWXC4l1BHE5Fu9S9qczankfAVzUJWg+ydpJ0hX/KGXkdcjsq2/vPevxZfx0NFyveguM/Q5e3vV7tz+FfPNfUKhukAVW02nt6L0NShzCq1ai8A75mfSB9DXz6L+vfsp4a3RFeH2kYY1p7eWsTrCFVPuxeBsvesF7H8CHgc4d///J3xz+lfOTege/uBnf7FvCJwM2vwaE0WO39FyBlH5/iPhEpBdwFVPf8HGPMw7ZMpQLj5H7rcMXO4y9qB75ra5Wl9zXWjn/T+15NwQJ6mnQVeC+vepkqxatwV727fP+kgkWtX4DeuQEqN4FqLfL8FBVYvv40WIi1YGxCT40eHrJ24LsHal1/0Z/+QEpNyhQrxL81/MvR7t27adCgwV8+/swzz/DVV195/VzPkw9GK/cOfC9c+8LF78BXugZ0esv69336sC3zqdz5ehqRwsaYHA5rUCHry2egcDy0fMyvT4+JsXb86/RWKp98t5e/Na0S4AEj0/PPa0uQF3fA9267d/3fge+K9pC+1toqtucnEOvrjzKVX74+0vhARO4TkUoiUtr9Zutkyn+b58C2z6DLxHztwBdfxNrx7/kFW9l2wMuOf1EqIyOD++67j/r163PjjTfy+++/07t3b2bNmgXAwoULqVOnDtdddx0PP/wwHTt2zPrcrVu30qpVK2rUqMEbb0TPJpjuHfgGXTUo/zvwtR4EMQXg/14IzHDKJ77+RDkHvAqs4I+npjTLDkWHtsHCx/4S8PlLw7/c7dixg/79+7NlyxZKlSrF7Nmzsy47c+YM999/P59//jmpqakcPvznp1G2bdvGF198werVq3nuuec4fz7y79tcAz5/ZYV/syFtQf5vT/nE18d0A4Faxpgjdg6j8unsKes49lwCPn91SazKup+P8diMDUzsmXTxz0HbrOF7DQN+m5t6bcrzOgkJCTRp0gSApKQkdu/enXXZtm3bqFGjBgkJ1rYzd9xxB5MmTcq6/Oabb6ZQoUIUKlSI8uXLc/DgQapWrRrQ/4dQ4zXg85c7/PuoK5SvC2VqBu62VY58XTS2AL/ZOYjKJ3fAd+nVXgM+fw3tWI+uE1cyYfEuHmgVWt+YvvyAt4PnKcljY2P/tANfXlV99s8N+9OZ58Ed8E3rOC3wO/C5w7/pPeDer6Cgn6+TKJ/4+vRUBrDeVYW/4X6zczB1kVaMhWM/QfsRttx8oQKxjO+eyJRlP7J8pz7gzEudOnXYtWtX1qOP6dOnOzuQg9wB3yvJr9i3A1+zPtaj608fsX6BUrbxddH4BPg31sZLeshtqNm9DJa97nPA5y8N/3xXpEgRxo0bR7t27bjuuuuoUKEC8fE27DAY4jwDvmYVm9n3hbLCv62wRrcutpNPp0YHEJEiwKXGmO0+37hIO+B1IBZ4xxgzPJfrNQdWAt2MMbO83aaeGj2bUwdgUivruHU/egx/jP1mJ1+lHXQs/AuH03wDnD59muLFi2OMoX///tSuXZsBAwY4MotT99mw5cM4ee4ko1JGBee1sKO7rPDvjmlQrbn9Xy+8BO/U6CJyC7Ae+K/r/SYiMj+Pz4kFxgLtgXrAHSJSL5frvQJ8cVGTqz8CvqS7g7ZggIZ/vnr77bdp0qQJ9evX58SJE9x///1OjxRU+Qr4/KXhn+18/TVxGNACOA5gjFkPJOTxOS2AncaYXcaYc8A0oFMO1/snMBvQPR0v1pfPQqESkPx4UL+sO/xbrDv+eTVgwADWr1/P1q1bmTp1KkWLFnV6pKBJ+yXtjx34/A34/HVFe2jcDWbrjn928HXRuGCMOZHtY3k9r1UF2OPxfrrrY1lEpArQGZjg4xzKbctc2LYAOucv4POXZ/i3/UDwt1bRPT98F+z76sTZEwxYNCAwAZ+/Wg8GiYFvXnTm60cwX3/abBaRO4FYEaktIm9ivSjuTU6PR7P/6x0DPGmMyfB6QyJ9RWStiKzNHklFpcPb4bNHoev7UNS5MN8d/vX78Nughn9xcXGcPn1aFw4fGGM4ffo0cXEBPsw1FwEP+PzlDv82zbLOjqACxtdO45/AYOAs8DHW6w95tfvpQDWP96sC+7JdpxkwzfV8Z1mgg4hcMMZ84nklY8wkYBJYL4T7OHNkOnvKOh79+uess3w6zB3+PT5zAxN6BCf8K126NEePHuXUKd080hdxcXGULh2cXy4mbZwU+IDPX8XKwj/ehY+6Qbk6Gv4FiM9HT130DYsUAL4H2gJ7gTXAncaYLblc/11ggR495YUx1gt8hUvCrW86PU2Wsxcy6DpxJe0bVKRfin5jRqtle5fxzLJnmNZxmn09hj/WvANrpsC9X0Z7+BeQ3+i8PtLI6wgpY8ytXi67ICIPYT0qiQWmGGO2iEg/1+X6OsbFWjkOju2GPqF1oJk7/Os0dhmNqsZzTU3d8S/a7Du9j8GpgxmZMjK0FgyAZvfAnjWwYID1GmCInQYn3Hh9pCEih7FezP4YWEW2lcoYs9jW6XIQtY80di+Dmb3g3q/hksucniZHqTuOMHDGeuY/dB0V4+2LDFVoOZtxll6f96J9Qnt61e/l9Dg5O/cbTL4BknpDi/ucnsYpQek0KgKDgAZYkd4NwBFjzGInFoyodeoAzL4H/jYhZBcMgOtql6XXNdV5cOq3nLuQ6fQ4KkiGrx5O5eKVL24HvmBz7/i3aLj1qEP5La89wjOMMf81xvQCrgJ2AotE5J9BmU55BHy9oXbwAj5/PZBSk9LFCvLSwjSnR1FB8MnOT1h7YG1wAz5/afgXEHkecisihUSkC/Ah0B94A5hj92DK5ctnoWBxSH7C6Ul8YoV/Tfhm+yHmrdfwL5Kl/ZLGa2tfY0xrBwI+f2n4l29eFw0ReQ+rx0gEnjPGNDfGvGCM0Z8GwbB5Dmz7FLpMciTg81d8kTjGd0/iuU+dCf+U/bICvisHUbNUmB0xp+FfvuT1k6gncDnwL2C5iJx0vZ0SEd3/006Ht1s78HX9wNGAz1/1KpdkcIfgh3/Kfn8K+BIcDPj8peFfvuT1mkaMMaaE662kx1sJY0zJYA0ZdUIs4PPXbUlVubZWGR6fuUHr7QgSUgGfv9zh3/yH4ZcfnJ4mrITPcx7RwhiY9xBcehUk9nR6mnwb2rEeB06eZeKSXU6PogJg2d5lzNw+k5EpIwO/A1+wVW0GrZ+G6T3h3K9OTxM2dNEINe6Ar/2rTk8SEO7wb3Lqjyz/QXf8C2fugM/WHfiCrdk9ULGhFf7po2Gf6KIRSn5aDqmjrRMR2rgDX7BVLlWE0V2b8Mi09Rw4ccbpcZQfgrYDX7CJQMfRcHCL7vjnI100QsWpAzCrT8gHfP7S8C+8hUXA56+CRa1f1DT884kuGqEgzAI+f2n4F57CKuDzV5ma1klANfzLky4aoSDMAj5/afgXfrYd3RZ+AZ+/6nTQ8M8Humg4LUwDPn9p+Bc+Tpw9wYBvwjTg85eGf3mK/J9SoSzMAz5/ucO/Bz78llMa/oWkTJPJoNRBtKrWKjwDPn9p+JcnXTSckhXwDQvrgM9ftyVV5eqaZXh85kYN/0LQ2xvf5tS5U+Ed8PlLwz+vdNFwgjEw/59Q7UpIjMCjUXz0zC312H/yDJM0/Aspy/cuZ8b2GZER8PlLw79c6aLhhJXj4Ogu6DDS6UkcVahALOO6J/L20h9Z8cMvTo+jsAK+QamDGJ48nPJFyzs9jrM0/MuRLhrBFqEBn7+qlCrC6G6N+de07zT8c9i5jHM8uuhRetfvTfOKzZ0ex3ka/uVIF41gygr4xsMl1Z2eJmS0rF2Ou66+jP4frdPwz0HDVw+nUvFKobtlqxM0/PsLXTSCJeM8zLwbEntB7RucnibkPNiqFpcUjdPwzyHzds5jzYE1PH/N85Eb8PnLM/z7Vc+fpotGsHw1zPqtJeVJpycJSRr+OWfb0W2MWjuK0a1GU7xgcafHCU11OkCjrtYzBZkZTk/jKF00gmHLXEibD13ejoqAz1/xReIY1z2R5z7dyvcHNfwLBnfA9/SVT1PrklpOjxPa2gyx/vt/0R3+6U8wux3eDp89aj0vGkUBn7/qV45nUIe69PtAwz+7uQO+lGoptE9o7/Q4oS8mFv4+BTbOiOrwTxcNO509bR3nff0wqNzU6WnCxt81/AuKtze+zcmzJ3k06VGnRwkfxcpC1/eiOvzTRcMuxsD8h6Bai6gO+Pyl4Z+9lu9dzvTt062ALzZKAz5/VW0GrZ6CGXfBud+cnibodNGwy8rxGvDlg4Z/9nEHfK8kv0KFYhWcHic8Nb8XKtSPyvBPFw07/LQCUl/TgC+fNPwLPHfA16t+Lw348kMEOo6BA5tg7WSnpwkqXTQC7dRBDfgCSMO/wBq+ejgVi1Wkd/3eTo8S/goWhW4fwDcvQ/pap6cJGl00AinjPMy623oNQwO+gNHwLzDcAV9E78AXbGVqwq1vRFX4p4tGIH01DOKKaMAXYBr+5Z8GfDaqczM0/DvMvicqwj9dNAJlyyca8NlIwz//uQO+p1o8pQGfXVoPAZMJ3/zb6Ulspz/dAuHw9/DZQA34bKbh38XLNJkMTh1MctVkOtTo4PQ4kSu2ANw2BTZMh20LnZ7GVrpo5NfZ09YOfG2f1YAvCP6eVJWrNPzz2Tub3uH42eM81uwxp0eJfMXLuXb8+2dEh3+6aORH1g58zSFJTycdLM/eUo/9J37n7aUa/nmzfN9ypm2bxqiUURrwBUu15hEf/tm6aIhIOxHZLiI7ReSpHC7vLiIbXW/LRaSxnfME3KoJ8MtODfiCrFCBWMb1SGLSkh9ZuUvDv5zsP72fQUsHMbzlcA34gq35vVC+nvWUdQQ+GrZt0RCRWGAs0B6oB9whIvWyXe1HIMUY0wh4AZhk1zwB9/NKWDrKOk47rojT00SdKqWK8FrXxjz88XccPKnhn6dzGecYuGggd9W/ixaVWjg9TvQRgVvGwP4NsHaK09MEnJ2PNFoAO40xu4wx54BpQCfPKxhjlhtjjrneXQlUtXGewDl10NpQqdM4DfgclHx5OXpedRn9p67jfIaGf26vrLZOD3J3/budHiV6FSwG3T6Eb16C9G+dniag7Fw0qgB7PN5Pd30sN/cAn9s4T2BkXLCK78SecPmNTk8T9fq3rkV8EQ3/3Ob/MJ9VB1ZpwBcKytSEW16Hmb0iKvyzc9HI6V9sjk/wiUhrrEUjxypORPqKyFoRWXv48OEAjuiHr4dBgUIa8IWImBjhta5N+DrtEPM37HN6HEdtO7qNkWtGMrrVaEoULOH0OAqgbkdocFtEhX92LhrpQDWP96sCf/muFpFGwDtAJ2NMjq9qGmMmGWOaGWOalStXzpZhfbLlE9gyD257x9qQRYWE+KJxjO+RyLD5W6I2/PMM+GpfUtvpcZSnNkMjKvyzc9FYA9QWkQQRKQjcDsz3vIKIXArMAXoaY763cZb8ywr43tOALwRFc/inAV+Ii7Dwz7ZFwxhzAXgI+AJIA2YYY7aISD8R6ee62jNAGWCciKwXkdA8VaRnwFcl0elpVC6iNfzTgC8MRFD4J+H2zdWsWTOzdm0Q1xZjrBe+CxaFTmOD93WVX85eyKDrhBXc3KgSfZNrOj2O7ZbvW86Q1CF8fPPH2mOEg9Vvw7fvwj1fWj9TgisgR0ZoEZ4XDfjCSjSFfxrwhaEICP900fBGA76wFA3hnwZ8YSoCwj9dNHKjAV9Yi/TwTwO+MBbm4Z8uGjnRgC8iRGr4pwFfBPhT+BdeT6PqopETDfgiQiSGf9uPbteAL1JkhX99wir800Uju63zrDcN+CKCZ/i3I8zDv5PnTjJg0QCebPGkBnyRos1Qa8H45iWnJ/GZLhqejuyABQPhHxrwRZL6leN5un0d7v8wfMO/TJPJ4KWDaVmlJTfXuNnpcVSgxBaAv/8HNkwLm/BPFw23rIBvqAZ8EegfzapxZUIZnpgVnuHf5E2TOXb2mAZ8kSjMwj9dNMA6XvrTh6FKM0jUHfgi1bO31GPv8d95Z+mPTo9yUVbsW8HH2z7WHfgiWRjt+KeLBsCqidZTUzePtI6jVhGpcFws47onMnHJLlaFSfh34NcDDErVgC8qhEn4p4vGzyth6Ujo+r4GfFGg6iVFrfBvWuiHf+6Ar2e9nhrwRYMwCf+ie9E4fcgV8I2F0glOT6OCJPnycnS/MvTDvxFrRlC+aHkN+KJJGIR/0btouAO+pj3g8pucnkYF2UOta1GySBwvL9zm9Cg5+vSHT1m5f6UGfNEoxMO/6F00vn4OYuOsF59U1ImJEUZ3bcKXaQdYsDG0wr/tR7fz6ppXNeCLZiEc/kXnorF1vrUL322TNeCLYvFF4xjfPYln5oVO+KcBn8oSouFf9C0aR3bAggHQ9V0N+BQNqsTzVIiEf+4d+K6rcp0GfMoj/PsYtn/u9DRZomvROHsapveENkOgSpLT06gQ0bVZNa5MKM2Ts50N/6ZsnsKxM8d4vNnjjs2gQow7/Jv3UMiEf9GzaGQFfEmQ1NvpaVSIefaW+qQfcy78W7FvBR+lfcTIlJEa8Kk/q9YipMK/6Fk0NOBTXjgZ/nkGfBWLVQzq11ZhIoTCv+hYNDTgUz5wIvzTgE/5JITCv8hfNDTgUxch2OGfBnzKZyES/kX2oqEBn/JDsMI/DfjURQuB8C+yFw0N+JQfghH+acCn/OZw+Be5i4YGfCof7Az/NOBT+eZg+BeZi4YGfCoA3OFfvw+/5fTZCwG5TQ34VEA4GP5F3qJx7lcN+FTAdG1WjRYJpXli1oaAhH8a8KmA8Qz/ju4K2peNrEXDGJj/sLVdqwZ8KkCevaU+e47+zuTU/IV/K/ev1IBPBVa1FpDyJEwPXvgXWYvG6klwZDvcPEoDPhUwheNiGd8jkQmL/Q//Dvx6gKeXPs3LLV/WgE8FVov7oHwd+OzRoIR/kbNo/LwSFo+Arh9owKcCruolRRnlCv8OXWT4dy7jHI8uepQedXtwZaUrbZpQRS0R6zDc/evh2//Y/uUiY9HQgE8FQcrl5bizxWX0/+jiwr8Ra0ZQtkhZ+jToY+N0KqoVLGb9wvx//4a99oZ/4b9oZAV83eGKdk5PoyLcP9vUonihAgz/3Lfwzx3wvXjdixrwKXuVrWWdamSGveFf+C8aWQHf005PoqJATIwwulsT/rc17/DPHfC91uo1DfhUcNS9BRp0gdn32Bb+hfeisXU+bJkLXd7RgE8FTamiBbPCv52Hcg7/Tp47ycBFA3mixRNcfsnlQZ5QRbU2z0DmBVj0si03H76LxpEdsOAR6PoeFCvj9DQqyjSoEs9T7epw/wd/Df8yTSZDUodwTeVr6Fijo0MTqqgVWwD+PgXWfwTb/xvwm7d10RCRdiKyXUR2ishfTgAlljdcl28UkUSfblgDPhUCujavRvPqpXly1p93/JuyeQq/nPmFJ5o/4eB0KqoVL28V4/P6Bzz8s23REJFYYCzQHqgH3CEi9bJdrT1Q2/XWFxjv043PfxgqN4UkPZ20ctawW+vz89HfssK/lftXMjVtKqNSRmnAp5x16ZWQ8kTAwz87H2m0AHYaY3YZY84B04BO2a7TCXjfWFYCpUSkktdb/fUwHNaAT4UG945/Exb/wOdpaTy99GndgU+FjhZ9odwVVvgXIAUCdkt/VQXY4/F+OpC9bMrpOlWA/bnd6M+/HeKhhLawVB/6q9BRu+FZnli2g6oFWjHpixgmscbpkZQCoGBmLwbv/ydVOwfm9uxcNHJ6GJC9cfflOohIX6ynrwDOjm3/n835nC0YygJHnB7CBzpnAG1mcVl4LtTnDIv7Ep0zYCYADJHNxpgG+b0tOxeNdKCax/tVgewHtvtyHYwxk4BJACKy1hjTLLCjBp7OGVg6Z+CEw4ygcwaaiKwNxO3Y+ZrGGqC2iCSISEHgdmB+tuvMB+5yHUV1FXDCGJPrU1NKKaWcZdsjDWPMBRF5CPgCiAWmGGO2iEg/1+UTgIVAB2An8Bugh0MppVQIs/PpKYwxC7EWBs+PTfD4swH6X+TNTgrAaMGgcwaWzhk44TAj6JyBFpA5JRC7kSmllIoO4XsaEaWUUkEXkouGiPxDRLaISKaINMt22dOu045sF5Gbcvn80iLypYjscP33kiDMPF1E1rvedovI+lyut1tENrmuF5CjGS6GiAwTkb0es3bI5XpeTwEThDlfFZFtrtPLzBWRUrlcL+j3p22nxwnsjNVE5BsRSXN9L/0rh+u0EpETHv8Wngn2nK45vP4dhsj9eYXH/bReRE6KyCPZruPI/SkiU0TkkIhs9viYTz8D/fo+N8aE3BtQF7gCWAQ08/h4PWADUAhIAH4AYnP4/BHAU64/PwW8EuT5RwHP5HLZbqCsg/ftMOCxPK4T67pvawAFXfd5vSDPeSNQwPXnV3L7Owz2/enLfYN1cMfnWB3SVcAqB/6eKwGJrj+XAL7PYc5WwIJgz3axf4ehcH/m8G/gAHBZKNyfQDKQCGz2+FiePwP9/T4PyUcaxpg0Y8z2HC7qBEwzxpw1xvyIddRVi1yu957rz+8Bf7Nl0ByIiABdgY+D9TVt4MspYGxljPmfMcZ9+tiVWA1PKLDn9DgBZozZb4xZ5/rzKSAN62wL4cjx+zObtsAPxpifHJwhizFmCXA024d9+Rno1/d5SC4aXuR22pHsKhhX7+H6b/kgzObWEjhojNmRy+UG+J+IfOsq3Z3wkOth/pRcHrb6ej8HSx+s3zRzEuz705f7JqTuPxGpDjQFVuVw8dUiskFEPheR+sGdLEtef4chdX9iNWe5/VIYCvcn+PYz0K/71dZDbr0Rka+AnM7qNtgYMy+3T8vhY0E7/MvHme/A+6OMa40x+0SkPPCliGxz/aYQlDmxziT8Atb99gLWU2nZN68Oyv3sy/0pIoOBC8DUXG7G9vszm4CdHicYRKQ4MBt4xBhzMtvF67CeYjntem3rE6wzTgdbXn+HoXR/FgRuBXLaKjRU7k9f+XW/OrZoGGOu9+PTfDrtCHBQRCoZY/a7HsYe8mfG7PKaWUQKAF2AXDf5MMbsc/33kIjMxXqIGNAfcr7etyLyNrAgh4t8vZ/zxYf7sxfQEWhrXE/C5nAbtt+f2QTs9Dh2E5E4rAVjqjFmTvbLPRcRY8xCERknImWNMUE9j5IPf4chcX+6tAfWGWMOZr8gVO5PF19+Bvp1v4bb01PzgdtFpJCIJGCt4qtzuV4v1597Abk9cgm064Ftxpj0nC4UkWIiUsL9Z6wXe4N68sVszwV3zuXr+3IKGFuJSDvgSeBWY0yOmwE4dH+GxelxXK+tTQbSjDGv5XKdiq7rISItsH4e/BK8KX3+O3T8/vSQ6zMJoXB/evDlZ6B/3+fBfqXfx6MBOmOtgmeBg8AXHpcNxnrFfzvQ3uPj7+A60gooA3wN7HD9t3SQ5n4X6JftY5WBha4/18A6QmEDsAXraZhg37cfAJuAja5/IJWyz+l6vwPWETc/ODTnTqznW9e73iaEyv2Z030D9HP/3WM97B/runwTHkcABvH+uw7rqYaNHvdhh2xzPuS63zZgHWxwjQNz5vh3GGr3p2uOoliLQLzHxxy/P7EWsf3AedfPzXty+xkYiO9zLcKVUkr5LNyenlJKKeUgXTSUUkr5TBcNpZRSPtNFQymllM900VBKKeUzXTSUChAReViss8rmVq8rFfb0kFulAkREtmG1Qz86PYtSdtFHGkoFgIhMwArV5ovIAKfnUcou+khDqQARkd1YtbIT5xpSKij0kYZSSimf6aKhlFLKZ7poKKWU8pkuGkoppXymL4QrpZTymT7SUEop5TNdNJRSSvlMFw2llFI+00VDKaWUz3TRUEop5TNdNJRSSvlMFw2llFI+00VDKaWUz/4fPvRRq4AxkaAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "f  = ctrl.Consequent(np.arange(-10, 11, 1), 'f')\n",
        "f.automf(3, names=[ 'low', 'meduim', 'high'])\n",
        "f.view()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "rule1 = ctrl.Rule(x['middle'] & v['fast'], f['high'])\n",
        "rule2 = ctrl.Rule(x['middle'] & v['slow'], f['low'])\n",
        "rule3 = ctrl.Rule(\n",
        "    (x['left'] & v['slow']) |\n",
        "    (x['left'] & v['middle']) |\n",
        "    (x['left'] & v['fast']), f['meduim'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "car_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])\n",
        "car = ctrl.ControlSystemSimulation(car_ctrl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "n_states = 40"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def obs_to_state(env, obs):\n",
        "    \"\"\" Maps an observation to state \"\"\"\n",
        "    env_low = env.observation_space.low\n",
        "    env_high = env.observation_space.high\n",
        "    env_dx = (env_high - env_low) / n_states\n",
        "    a = int((obs[0] - env_low[0])/env_dx[0])\n",
        "    b = int((obs[1] - env_low[1])/env_dx[1])\n",
        "    return a, b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----- Start -----\n",
            "Iteration: 0 -- x: 13 -- v: 20 , obs: [-5.886063e-01  5.216941e-04] -- action: 0.022256331542594095 -- reward: -4.9534429373386886e-05\n",
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            "Iteration: 130 -- x: 32 -- v: 22 , obs: [0.25082707 0.00692274] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 131 -- x: 32 -- v: 21 , obs: [0.25742483 0.00659775] -- action: 5.189743589743592 -- reward: -2.69334385272847\n",
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            "Iteration: 140 -- x: 33 -- v: 21 , obs: [0.30758208 0.00512906] -- action: 5.189743589743592 -- reward: -2.69334385272847\n",
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            "Iteration: 153 -- x: 35 -- v: 22 , obs: [0.38554615 0.00770479] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 154 -- x: 35 -- v: 22 , obs: [0.3937449  0.00819874] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 155 -- x: 35 -- v: 22 , obs: [0.40249416 0.00874929] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 156 -- x: 35 -- v: 22 , obs: [0.411855   0.00936085] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 157 -- x: 35 -- v: 22 , obs: [0.4218934  0.01003838] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 158 -- x: 36 -- v: 22 , obs: [0.43268076 0.01078737] -- action: 5.3131951762088745 -- reward: -2.823004298048925\n",
            "Iteration: 159 -- x: 36 -- v: 23 , obs: [0.44429466 0.01161391] -- action: 5.373931623931625 -- reward: -2.8879141098692394\n",
            "Iteration: 160 -- x: 36 -- v: 23 , obs: [0.4568194  0.01252473] -- action: 5.373931623931625 -- reward: 97.11208589013076\n",
            "finish in 160 steps\n"
          ]
        }
      ],
      "source": [
        "env_name = 'MountainCarContinuous-v0'\n",
        "env = gym.make(env_name)\n",
        "env = gym.wrappers.Monitor(env, \"./MountainCarContinuous-v0-results\", force=True)\n",
        "\n",
        "env.seed(0)\n",
        "np.random.seed(0)\n",
        "\n",
        "print ('----- Start -----')\n",
        "\n",
        "frames = []\n",
        "obs = env.reset()\n",
        "\n",
        "\n",
        "for iteration in range(0, 200):\n",
        "    frames.append(env.render(mode = 'rgb_np'))\n",
        "    x, v = obs_to_state(env, obs)\n",
        "\n",
        "    car.input['x'] = x\n",
        "    car.input['v'] = v\n",
        "\n",
        "    car.compute()\n",
        "    \n",
        "    fuzzy_action = car.output['f']\n",
        "\n",
        "    obs, reward, done, _ = env.step([fuzzy_action]) \n",
        "    \n",
        "    print(f'Iteration: {iteration} -- x: {x} -- v: {v} , obs: {obs} -- action: {fuzzy_action} -- reward: {reward}')\n",
        "    \n",
        "    if obs[0]>=0.45:\n",
        "        print(\"finish in %d steps\" %(iteration))\n",
        "        delay(5000)\n",
        "        break\n",
        "\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
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    "colab": {
      "name": "NN_HW6_FuzzyFan(Q2).ipynb",
      "provenance": [],
      "toc_visible": true
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