examples/example_colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_q2As9YqDMMa"
   },
   "source": [
    "# Install blobrl for Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "id": "O-pc67vODVzE",
    "outputId": "e03297e1-4845-4066-d7b3-abbd0c52db5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'reinforcement'...\n",
      "remote: Enumerating objects: 462, done.\u001b[K\n",
      "remote: Counting objects: 100% (462/462), done.\u001b[K\n",
      "remote: Compressing objects: 100% (292/292), done.\u001b[K\n",
      "remote: Total 1549 (delta 279), reused 310 (delta 163), pack-reused 1087\u001b[K\n",
      "Receiving objects: 100% (1549/1549), 668.47 KiB | 2.04 MiB/s, done.\n",
      "Resolving deltas: 100% (973/973), done.\n",
      "/content/reinforcement\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/french-ai/reinforcement\n",
    "%cd reinforcement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "sCMnjYNqDdSu",
    "outputId": "742f036d-6f96-4eb5-87da-7ff68a00797a"
   },
   "outputs": [],
   "source": [
    "#!git checkout develop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "pU-dMHQ8ETW9",
    "outputId": "77001453-cf9a-4db0-cdc9-707b0a698308"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /content/reinforcement\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from blobrl==0.0.0) (1.6.0+cu101)\n",
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      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->blobrl==0.0.0) (1.18.5)\n",
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      "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel->blobrl==0.0.0) (5.3.5)\n",
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      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard->blobrl==0.0.0) (4.1.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->blobrl==0.0.0) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard->blobrl==0.0.0) (1.7.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard->blobrl==0.0.0) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard->blobrl==0.0.0) (1.24.3)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard->blobrl==0.0.0) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard->blobrl==0.0.0) (2020.6.20)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->IPython->blobrl==0.0.0) (0.2.5)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->IPython->blobrl==0.0.0) (0.6.0)\n",
      "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->IPython->blobrl==0.0.0) (0.2.0)\n",
      "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel->blobrl==0.0.0) (19.0.2)\n",
      "Requirement already satisfied: jupyter-core>=4.6.0 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel->blobrl==0.0.0) (4.6.3)\n",
      "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard->blobrl==0.0.0) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->blobrl==0.0.0) (3.1.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->blobrl==0.0.0) (3.1.0)\n",
      "Building wheels for collected packages: blobrl\n",
      "  Building wheel for blobrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for blobrl: filename=blobrl-0.0.0-cp36-none-any.whl size=45276 sha256=757f5e93502a49588f0cd06bfa1ae7bbb0c2847795971d781f1396022c5f2695\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-u0zrwxm2/wheels/e9/87/3d/ca0655202a0cb715f2d4a7a1e9728a1d6c60944fa503d9053b\n",
      "Successfully built blobrl\n",
      "Installing collected packages: blobrl\n",
      "Successfully installed blobrl-0.0.0\n",
      "/content\n"
     ]
    }
   ],
   "source": [
    "!pip install .[notebook]\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 678
    },
    "colab_type": "code",
    "id": "hbvz39JMEv0K",
    "outputId": "a018df81-e2b1-4aac-eea0-c68c113e619f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "The following package was automatically installed and is no longer required:\n",
      "  libnvidia-common-440\n",
      "Use 'apt autoremove' to remove it.\n",
      "The following additional packages will be installed:\n",
      "  xserver-common\n",
      "Suggested packages:\n",
      "  libgle3\n",
      "Recommended packages:\n",
      "  xfonts-base\n",
      "The following NEW packages will be installed:\n",
      "  python-opengl xvfb\n",
      "The following packages will be upgraded:\n",
      "  xserver-common\n",
      "1 upgraded, 2 newly installed, 0 to remove and 73 not upgraded.\n",
      "Need to get 1,307 kB of archives.\n",
      "After this operation, 7,682 kB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python-opengl all 3.1.0+dfsg-1 [496 kB]\n",
      "Get:2 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 xserver-common all 2:1.19.6-1ubuntu4.6 [26.9 kB]\n",
      "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 xvfb amd64 2:1.19.6-1ubuntu4.6 [783 kB]\n",
      "Fetched 1,307 kB in 0s (6,784 kB/s)\n",
      "Selecting previously unselected package python-opengl.\n",
      "(Reading database ... 144579 files and directories currently installed.)\n",
      "Preparing to unpack .../python-opengl_3.1.0+dfsg-1_all.deb ...\n",
      "Unpacking python-opengl (3.1.0+dfsg-1) ...\n",
      "Preparing to unpack .../xserver-common_2%3a1.19.6-1ubuntu4.6_all.deb ...\n",
      "Unpacking xserver-common (2:1.19.6-1ubuntu4.6) over (2:1.19.6-1ubuntu4.4) ...\n",
      "Selecting previously unselected package xvfb.\n",
      "Preparing to unpack .../xvfb_2%3a1.19.6-1ubuntu4.6_amd64.deb ...\n",
      "Unpacking xvfb (2:1.19.6-1ubuntu4.6) ...\n",
      "Setting up xserver-common (2:1.19.6-1ubuntu4.6) ...\n",
      "Setting up python-opengl (3.1.0+dfsg-1) ...\n",
      "Setting up xvfb (2:1.19.6-1ubuntu4.6) ...\n",
      "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
     ]
    }
   ],
   "source": [
    "!apt-get update > /dev/null 2>&1\n",
    "!apt-get install -y xvfb python-opengl\n",
    "!pip -q install pyopengl pyvirtualdisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zaMls7TREpvP"
   },
   "source": [
    "Start display for colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C56oQsm1EoxM"
   },
   "outputs": [],
   "source": [
    "# Start virtual display\n",
    "from pyvirtualdisplay import Display\n",
    "display = Display(visible=0, size=(512, 512))\n",
    "display.start()\n",
    "import os\n",
    "os.environ[\"DISPLAY\"] = \":\" + str(display.display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZTuJfCB_CYzh"
   },
   "source": [
    "## OpenAI Gym Available Environment\n",
    "\n",
    "Gym comes with a diverse suite of environments that range from easy to difficult and involve many different kinds of data. View the [full list of environments](https://gym.openai.com/envs) to get the birds-eye view.\n",
    "\n",
    "- [Classic control](https://gym.openai.com/envs#classic_control) and [toy text](https://gym.openai.com/envs#toy_text): complete small-scale tasks, mostly from the RL literature. They’re here to get you started.\n",
    "\n",
    "- [Algorithmic](https://gym.openai.com/envs#algorithmic): perform computations such as adding multi-digit numbers and reversing sequences. One might object that these tasks are easy for a computer. The challenge is to learn these algorithms purely from examples. These tasks have the nice property that it’s easy to vary the difficulty by varying the sequence length.\n",
    "\n",
    "- [Atari](https://gym.openai.com/envs#atari): play classic Atari games. \n",
    "\n",
    "- [2D and 3D robots](https://gym.openai.com/envs#mujoco): control a robot in simulation. These tasks use the MuJoCo physics engine, which was designed for fast and accurate robot simulation. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uUhS0_4ADCyx"
   },
   "source": [
    "Don't forget to set matplotlib to inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HO3nA-oZDCyz"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NEnwOnr6qHbO"
   },
   "source": [
    "# CartPole-v1 exemple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fj9G5c5kqM_y"
   },
   "source": [
    "## Initialize environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5Q7p1iRwdGpE"
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('CartPole-v1')\n",
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mxUtaISFqQ4Q"
   },
   "source": [
    "## Initialize agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "srJt2AZPp58A"
   },
   "outputs": [],
   "source": [
    "from blobrl.agents import AgentRandom\n",
    "action_space = env.action_space\n",
    "observation_space = env.observation_space\n",
    "agent = AgentRandom(observation_space=observation_space, action_space=action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EX_NctEhqfgu"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fldGbNR2qlDo"
   },
   "source": [
    "Create Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "CY1LF52LqeyH",
    "outputId": "636bda1e-8e66-4f75-d20a-b5f7dd00753d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/blobrl/trainer.py:28: UserWarning: be sure of agent have good input and output dimension\n",
      "  warnings.warn(\"be sure of agent have good input and output dimension\")\n"
     ]
    }
   ],
   "source": [
    "from blobrl import Trainer\n",
    "trainer = Trainer(environment=env, agent=agent, log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cGq2ksiRqkRR"
   },
   "source": [
    "Start train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "id": "0BACJeOjqkXO",
    "outputId": "7e196d0e-d0f0-4f97-d475-00ee7e8397fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train(max_episode=10, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "id": "zcsTsAXrDCzY",
    "outputId": "63c7d9f6-6acb-4d9e-a0a4-fc72f2c5d53f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5zdti968DCzb"
   },
   "source": [
    "# MountainCar-v0 exemple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DKXo8BlnDCzc"
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('MountainCar-v0')\n",
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uwMJm7NcDCzg"
   },
   "source": [
    "## Initialize agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5MT1Rda6DCzh"
   },
   "outputs": [],
   "source": [
    "from blobrl.agents import AgentRandom\n",
    "action_space = env.action_space\n",
    "observation_space = env.observation_space\n",
    "agent = AgentRandom(observation_space=observation_space, action_space=action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HqR-B3JnDCzl"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "krhGlQ_0DCzm"
   },
   "source": [
    "Create Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "uq6iXWPyDCzm",
    "outputId": "8f806e79-7bb6-41aa-bc86-1e199351e269"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/blobrl/trainer.py:28: UserWarning: be sure of agent have good input and output dimension\n",
      "  warnings.warn(\"be sure of agent have good input and output dimension\")\n"
     ]
    }
   ],
   "source": [
    "from blobrl import Trainer\n",
    "trainer = Trainer(environment=env, agent=agent, log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "51WV8opDDCzq"
   },
   "source": [
    "Start train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "id": "Ow1IDG1EDCzr",
    "outputId": "bb1fedd4-dd5f-41c3-840b-5233b5d9f5ce"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BdEofMXrHzRP"
   },
   "source": [
    "# Copy-v0 exemple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3jnp42x8HzRR"
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('Copy-v0')\n",
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "es7gTbU7HzRU"
   },
   "source": [
    "## Initialize agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VjpqaWv5HzRV"
   },
   "outputs": [],
   "source": [
    "from blobrl.agents import AgentRandom\n",
    "action_space = env.action_space\n",
    "observation_space = env.observation_space\n",
    "agent = AgentRandom(observation_space=observation_space, action_space=action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dIi6KhNbHzRX"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e-5aWZ9PHzRZ"
   },
   "source": [
    "Create Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "XbGA_UpEHzRa",
    "outputId": "1228c0a8-16bd-496e-fc6b-5be68bc292b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/blobrl/trainer.py:28: UserWarning: be sure of agent have good input and output dimension\n",
      "  warnings.warn(\"be sure of agent have good input and output dimension\")\n"
     ]
    }
   ],
   "source": [
    "from blobrl import Trainer\n",
    "trainer = Trainer(environment=env, agent=agent, log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dipSqkC1HzRc"
   },
   "source": [
    "Start train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "TghfDktPHzRd",
    "outputId": "b622d201-b0bc-4e5b-eeed-31c42e01fced"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total length of input instance: 3, step: 1\n",
      "==========================================\n",
      "Observation Tape    :  \u001b[42m \u001b[0mDDD  \n",
      "Output Tape         :   \n",
      "Targets             :   DDD  \n",
      "\n",
      "Current reward      :   0.000\n",
      "Cumulative reward   :   0.000\n",
      "Action              :   Tuple(move over input: left,\n",
      "                              write to the output tape: False,\n",
      "                              prediction: B)\n",
      "Total length of input instance: 3, step: 1\n",
      "==========================================\n",
      "Observation Tape    :  \u001b[42m \u001b[0mDDD  \n",
      "Output Tape         :   \n",
      "Targets             :   DDD  \n",
      "\n",
      "Current reward      :   0.000\n",
      "Cumulative reward   :   0.000\n",
      "Action              :   Tuple(move over input: left,\n",
      "                              write to the output tape: False,\n",
      "                              prediction: B)\n"
     ]
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o5FwD5EpHzRh"
   },
   "source": [
    "# FrozenLake-v0 example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I6_NYqwOHzRj"
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('FrozenLake-v0')\n",
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FbnxCC3GHzRm"
   },
   "source": [
    "## Initialize agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PaP2Oy52HzRm"
   },
   "outputs": [],
   "source": [
    "from blobrl.agents import AgentRandom\n",
    "action_space = env.action_space\n",
    "observation_space = env.observation_space\n",
    "agent = AgentRandom(observation_space=observation_space, action_space=action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "amTu4_sfHzRq"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "txJGk3PdHzRq"
   },
   "source": [
    "Create Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "4YMMHmSGHzRr",
    "outputId": "0eb95257-3783-41ea-9370-7052cfc56805"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/blobrl/trainer.py:28: UserWarning: be sure of agent have good input and output dimension\n",
      "  warnings.warn(\"be sure of agent have good input and output dimension\")\n"
     ]
    }
   ],
   "source": [
    "from blobrl import Trainer\n",
    "trainer = Trainer(environment=env, agent=agent, log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ThlFeQLUHzRu"
   },
   "source": [
    "Start train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "colab_type": "code",
    "id": "qCo0WZcYHzRv",
    "outputId": "e1365f00-9c61-4a34-eb21-1e3bf97a09dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (Left)\n",
      "SFFF\n",
      "FH\u001b[41mF\u001b[0mH\n",
      "FFFH\n",
      "HFFG\n",
      "  (Left)\n",
      "SFFF\n",
      "FH\u001b[41mF\u001b[0mH\n",
      "FFFH\n",
      "HFFG\n"
     ]
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "colab_type": "code",
    "id": "6VCk3-dUHzRy",
    "outputId": "1270145d-bcc8-46cd-f9c8-8226e5246706"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (Up)\n",
      "SFFF\n",
      "\u001b[41mF\u001b[0mHFH\n",
      "FFFH\n",
      "HFFG\n",
      "  (Up)\n",
      "SFFF\n",
      "\u001b[41mF\u001b[0mHFH\n",
      "FFFH\n",
      "HFFG\n"
     ]
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "elk52tA1DCz0"
   },
   "source": [
    "# Assault-v0 example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sLlZ-tcDDCz1"
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('Assault-v0')\n",
    "_ = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_kq9c2BmDCz5"
   },
   "source": [
    "## Initialize agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O_4ISgMxDCz6"
   },
   "outputs": [],
   "source": [
    "from blobrl.agents import AgentRandom\n",
    "action_space = env.action_space\n",
    "observation_space = env.observation_space\n",
    "agent = AgentRandom(observation_space=observation_space, action_space=action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IpyN0XmnDCz-"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QNw-uWE6DCz_"
   },
   "source": [
    "Create Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "FbHz6FKMDCz_",
    "outputId": "1c090033-22c6-40d6-f0d5-d190a66e2f0a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/blobrl/trainer.py:28: UserWarning: be sure of agent have good input and output dimension\n",
      "  warnings.warn(\"be sure of agent have good input and output dimension\")\n"
     ]
    }
   ],
   "source": [
    "from blobrl import Trainer\n",
    "trainer = Trainer(environment=env, agent=agent, log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ALgfU_DdDC0C"
   },
   "source": [
    "Start train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "colab_type": "code",
    "id": "TwwRLMucDC0E",
    "outputId": "8b19e83a-be3c-4a40-addd-a3161aa58ec7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train(max_episode=1, nb_evaluation=0, render=True)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Exemple_colab.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}