test_env.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nbcdrYXQKmsZ"
   },
   "source": [
    "# Github version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 731,
     "status": "ok",
     "timestamp": 1558539032387,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "LHFe4AIHLiI9",
    "outputId": "f9880268-433f-445a-ab67-0fafea902427"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 648,
     "status": "ok",
     "timestamp": 1558539060197,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "7orXpXJALrw1",
    "outputId": "cc120305-ef6f-4bd9-b1fb-bfb603430515"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/Machine Learning/Github\n"
     ]
    }
   ],
   "source": [
    "cd drive/My\\ Drive/Machine\\ Learning/Github"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LM9xsCpMLqar"
   },
   "source": [
    "## Parallel envs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rQXxb2xeLsQe"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from parallel_envs.make_atari import make_atari_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qg6swF90LubP"
   },
   "outputs": [],
   "source": [
    "envs = make_atari_env('MontezumaRevengeNoFrameskip-v4', num_env=3, seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 573,
     "status": "ok",
     "timestamp": 1558539491869,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "wVTMHeciLyQQ",
    "outputId": "0858a475-f9be-459f-fdd6-8890bb48a705"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 84, 84)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = envs.reset()\n",
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ULZnvOpsL1qK"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_frame(s, num_env):\n",
    "    fig, axs = plt.subplots(nrows=num_env, ncols=4, figsize=(14,14))\n",
    "    plt.gray()\n",
    "    for i in range(3):\n",
    "        for j in range(4):\n",
    "            axs[i,j].imshow(s[i,j])\n",
    "            axs[i,j].grid(False)\n",
    "            axs[i,j].set_title('env:{0}, frame:{1}'.format(i,j+1))\n",
    "            axs[i,j].tick_params(labelbottom=False, labelleft=False, labelright=False, labeltop=False)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 825
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2194,
     "status": "ok",
     "timestamp": 1558539514334,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "TOSoYLnyMFuN",
    "outputId": "5db5522a-a545-4f1d-f4f6-d68535149d6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 17, 14]\n",
      "[0. 0. 0.]\n",
      "[False False False]\n",
      "({'ale.lives': 6}, {'ale.lives': 6}, {'ale.lives': 6})\n"
     ]
    },
    {
     "data": {
      "image/png": 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uvk583biduN1YR9TdlS6rUmtV6ixzrWWY2cSsmJUq1FqGrIB5SVFrVeosc63m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\nNiKSJEmSkrMRkSRJkpScjYgkSZKk5P4/tlV5S+Q6mUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x1008 with 12 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "actions = [envs.action_space.sample() for _ in range(3)]\n",
    "print(actions)\n",
    "obs, r, dones, infos = envs.step(actions)\n",
    "print(r)\n",
    "print(dones)\n",
    "print(infos)\n",
    "\n",
    "plot_frame(obs, num_env=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IlSLanJ6L2bk"
   },
   "source": [
    "## Env for eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lfcuz1RcLxYK"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from parallel_envs.make_atari import make_atari_env_for_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fjhRIOL4Lzv-"
   },
   "outputs": [],
   "source": [
    "env = make_atari_env_for_eval('MontezumaRevengeNoFrameskip-v4', seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2023,
     "status": "ok",
     "timestamp": 1558539086630,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "jlSTrUXnL1Mc",
    "outputId": "706839f1-53c7-4a66-bff1-5b38c9abe040"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(210, 160, 3)\n",
      "(1, 4, 84, 84)\n"
     ]
    }
   ],
   "source": [
    "_s, s = env.reset()\n",
    "print(_s.shape)\n",
    "print(s.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SfnxLaN08n7J"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_original_and_gray_stack(o_frame, frames):\n",
    "    fig, axs = plt.subplots(1, ncols=5, figsize=(20,14))\n",
    "    \n",
    "    for i in range(4):\n",
    "        axs[i].imshow(frames[0,i], plt.cm.gray)\n",
    "        axs[i].tick_params(labelbottom=False, labelleft=False, labelright=False, labeltop=False)\n",
    "      \n",
    "    axs[4].imshow(o_frame)\n",
    "    axs[4].tick_params(labelbottom=False, labelleft=False, labelright=False, labeltop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1734,
     "status": "ok",
     "timestamp": 1558539095145,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "f4s7whAJ9a_T",
    "outputId": "eeae5b45-339f-4d43-877e-dbbeb2f3020d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XX55k5baHL7zwQpLk6quvXvFv87V6fP3++vOax6mPW9fRrK32jW9845xzWqv+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\n/x/CfX5ilHyKvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1008 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_original_and_gray_stack(_s, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "paiMTWft-B1N"
   },
   "outputs": [],
   "source": [
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1637,
     "status": "ok",
     "timestamp": 1558537821420,
     "user": {
      "displayName": "星野悠一郎",
      "photoUrl": "",
      "userId": "01671396302619379298"
     },
     "user_tz": -540
    },
    "id": "nTK_8q7a9gwo",
    "outputId": "ee3a2ef8-8ca5-4488-d18d-e045816bb36e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "(210, 160, 3)\n",
      "(1, 4, 84, 84)\n",
      "0.0\n",
      "False\n",
      "{'ale.lives': 6}\n"
     ]
    },
    {
     "data": {
      "image/png": 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ddcMvbAl+6Zd+KUmyZs2aOcf0cg2xeLLZP/UCqjfddFOS5IADpvrojz/+eJKp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\nANACmjgAAAAALaCJAwAAANAC/x8Uf2vP78m6zgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1008 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "action = env.action_space.sample()\n",
    "print(action)\n",
    "(_ob, ob), r, dones, infos = env.step(action)\n",
    "\n",
    "print(_ob.shape)\n",
    "print(ob.shape)\n",
    "print(r)\n",
    "print(dones)\n",
    "print(infos)\n",
    "\n",
    "plot_original_and_gray_stack(_ob, np.array(ob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wqme0JNP97kg"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "test_env_for_eval.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}