t81_558_class_12_2_qlearningreinforcement.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PF0TtkQ4-NoP"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/t81_558_class_12_2_qlearningreinforcement.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dkLTudD-NoQ"
      },
      "source": [
        "# T81-558: Applications of Deep Neural Networks\n",
        "**Module 12: Reinforcement Learning**\n",
        "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
        "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wN-T0x2j-NoR"
      },
      "source": [
        "# Module 12 Video Material\n",
        "\n",
        "* Part 12.1: Introduction to Introduction to Gymnasium [[Video]](https://www.youtube.com/watch?v=FvuyrpzvwdI&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_12_1_reinforcement.ipynb)\n",
        "* **Part 12.2: Introduction to Q-Learning** [[Video]](https://www.youtube.com/watch?v=VKuqvbG_KAw&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_12_2_qlearningreinforcement.ipynb)\n",
        "* Part 12.3: Stable Baselines Q-Learning [[Video]](https://www.youtube.com/watch?v=kl7zsCjULN0&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_12_3_pytorch_reinforce.ipynb)\n",
        "* Part 12.4: Atari Games with Stable Baselines Neural Networks [[Video]](https://www.youtube.com/watch?v=maLA1_d4pzQ&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_12_4_atari.ipynb)\n",
        "* Part 12.5: Future of Reinforcement Learning [[Video]](https://www.youtube.com/watch?v=-euo5pTjP8E&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_12_5_rl_future.ipynb)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIOgB0oG-NoS"
      },
      "source": [
        "# Google CoLab Instructions\n",
        "\n",
        "The following code ensures that Google CoLab is running the correct version of TensorFlow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERfMETL_-NoS",
        "outputId": "14ac4f5d-ce91-4e6b-d36f-19f23626d937"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Note: using Google CoLab\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    from google.colab import drive\n",
        "    COLAB = True\n",
        "    print(\"Note: using Google CoLab\")\n",
        "except:\n",
        "    print(\"Note: not using Google CoLab\")\n",
        "    COLAB = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcTY1gkD-X5r",
        "outputId": "3d800245-3c10-4223-edfc-52688cc35bf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: gymnasium[accept-rom-license,atari] in /usr/local/lib/python3.10/dist-packages (0.29.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (1.23.5)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (2.2.1)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (4.5.0)\n",
            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.0.4)\n",
            "Requirement already satisfied: autorom[accept-rom-license]~=0.4.2 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.4.2)\n",
            "Requirement already satisfied: shimmy[atari]<1.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.2.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (8.1.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (4.66.1)\n",
            "Requirement already satisfied: AutoROM.accept-rom-license in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (0.6.1)\n",
            "Requirement already satisfied: ale-py~=0.8.1 in /usr/local/lib/python3.10/dist-packages (from shimmy[atari]<1.0,>=0.1.0->gymnasium[accept-rom-license,atari]) (0.8.1)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from ale-py~=0.8.1->shimmy[atari]<1.0,>=0.1.0->gymnasium[accept-rom-license,atari]) (6.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2023.7.22)\n",
            "Requirement already satisfied: pyvirtualdisplay in /usr/local/lib/python3.10/dist-packages (3.0)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n",
            "xvfb is already the newest version (2:21.1.4-2ubuntu1.7~22.04.2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "# HIDE OUTPUT\n",
        "if COLAB:\n",
        "  !pip install gymnasium[accept-rom-license,atari]\n",
        "  !pip install pyvirtualdisplay\n",
        "  !sudo apt-get install -y xvfb ffmpeg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zA-E4NR2-NoY"
      },
      "source": [
        "# Part 12.2: Introduction to Q-Learning\n",
        "\n",
        "Q-Learning is a foundational technology upon which deep reinforcement learning is based. Before we explore deep reinforcement learning, it is essential to understand Q-Learning. Several components make up any Q-Learning system.\n",
        "\n",
        "* **Agent** - The agent is an entity that exists in an environment that takes actions to affect the state of the environment, to receive rewards.\n",
        "* **Environment** - The environment is the universe that the agent exists in. The environment is always in a specific state that is changed by the agent's actions.\n",
        "* **Actions** - Steps that the agent can perform to alter the environment\n",
        "* **Step** - A step occurs when the agent performs an action and potentially changes the environment state.\n",
        "* **Episode** - A chain of steps that ultimately culminates in the environment entering a terminal state.\n",
        "* **Epoch** - A training iteration of the agent that contains some number of episodes.\n",
        "* **Terminal State** -  A state in which further actions do not make sense. A terminal state occurs when the agent has one, lost, or the environment exceeds the maximum number of steps in many environments.\n",
        "\n",
        "Q-Learning works by building a table that suggests an action for every possible state. This approach runs into several problems. First, the environment is usually composed of several continuous numbers, resulting in an infinite number of states. Q-Learning handles continuous states by binning these numeric values into ranges.\n",
        "\n",
        "Out of the box, Q-Learning does not deal with continuous inputs, such as a car's accelerator that can range from released to fully engaged. Additionally, Q-Learning primarily deals with discrete actions, such as pressing a joystick up or down. Researchers have developed clever tricks to allow Q-Learning to accommodate continuous actions.\n",
        "\n",
        "Deep neural networks can help solve the problems of continuous environments and action spaces. In the next section, we will learn more about deep reinforcement learning. For now, we will apply regular Q-Learning to the Mountain Car problem from OpenAI Gym.\n",
        "\n",
        "## Introducing the Mountain Car\n",
        "\n",
        "This section will demonstrate how Q-Learning can create a solution to the mountain car gym environment. The Mountain car is an environment where a car must climb a mountain. Because gravity is stronger than the car's engine, it cannot merely accelerate up the steep slope even with full throttle. The vehicle is situated in a valley and must learn to utilize potential energy by driving up the opposite hill before the car can make it to the goal at the top of the rightmost hill.\n",
        "\n",
        "First, it might be helpful to visualize the mountain car environment. The following code shows this environment. This code makes use of TF-Agents to perform this render. Usually, we use TF-Agents for the type of deep reinforcement learning that we will see in the next module. However, TF-Agents is just used to render the mountain care environment for now."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "SkzXb-4kB0oP",
        "outputId": "569ee4c3-24be-41c6-b07b-8b26a1c8572e"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=600x400>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import gymnasium as gym\n",
        "from PIL import Image\n",
        "\n",
        "# Create and initialize the MountainCar environment\n",
        "env = gym.make('MountainCar-v0', render_mode=\"rgb_array\")\n",
        "env.reset()\n",
        "\n",
        "# Render the environment's state to a numpy array\n",
        "frame = env.render()\n",
        "\n",
        "# Convert the numpy array to an image and display it\n",
        "image = Image.fromarray(frame)\n",
        "\n",
        "# Don't forget to close the environment when you're done!\n",
        "env.close()\n",
        "\n",
        "display(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQBcs3yAMo_P"
      },
      "source": [
        "The mountain car environment provides the following discrete actions:\n",
        "\n",
        "* 0 - Apply left force\n",
        "* 1 - Apply no force\n",
        "* 2 - Apply right force\n",
        "\n",
        "The mountain car environment is made up of the following continuous values:\n",
        "\n",
        "* state[0] - Position\n",
        "* state[1] - Velocity\n",
        "\n",
        "The cart is not strong enough. It will need to use potential energy from the mountain behind it. The following code shows an agent that applies full throttle to climb the hill."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wYTpj1_RigEv",
        "outputId": "94e33a3a-cb14-4817-b75b-2bf0644478c5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/record_video.py:94: UserWarning: \u001b[33mWARN: Overwriting existing videos at /content/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
            "  logger.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Step 1: State=[-0.51047415  0.0009084 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 2: State=[-0.5086642   0.00180999], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 3: State=[-0.5059661   0.00269802], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 4: State=[-0.50240034  0.00356583], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 5: State=[-0.49799338  0.00440695], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 6: State=[-0.49277827  0.0052151 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 7: State=[-0.486794    0.00598428], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 8: State=[-0.4800852  0.0067088], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 9: State=[-0.47270182  0.00738338], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 10: State=[-0.46469867  0.00800313], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 11: State=[-0.456135    0.00856367], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 12: State=[-0.44707385  0.00906114], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 13: State=[-0.43758163  0.00949224], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 14: State=[-0.42772737  0.00985424], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 15: State=[-0.4175823   0.01014508], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 16: State=[-0.40721902  0.01036329], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 17: State=[-0.39671093  0.01050807], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 18: State=[-0.3861317   0.01057923], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 19: State=[-0.3755545  0.0105772], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 20: State=[-0.36505148  0.01050302], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 21: State=[-0.35469326  0.01035824], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 22: State=[-0.34454829  0.01014497], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 23: State=[-0.33468252  0.00986574], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 24: State=[-0.325159    0.00952351], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 25: State=[-0.31603745  0.00912158], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 26: State=[-0.3073739   0.00866354], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 27: State=[-0.29922068  0.00815321], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 28: State=[-0.2916261   0.00759461], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 29: State=[-0.2846342   0.00699189], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 30: State=[-0.27828494  0.00634927], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 31: State=[-0.27261388  0.00567106], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 32: State=[-0.2676523   0.00496156], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 33: State=[-0.2634272   0.00422511], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 34: State=[-0.25996122  0.00346599], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 35: State=[-0.2572727  0.0026885], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 36: State=[-0.2553758  0.0018969], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 37: State=[-0.25428042  0.00109539], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 38: State=[-0.2539922  0.0002882], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 39: State=[-0.2545127  -0.00052048], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 40: State=[-0.25583917 -0.00132646], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 41: State=[-0.2579647  -0.00212556], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 42: State=[-0.26087826 -0.00291354], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 43: State=[-0.26456445 -0.00368619], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 44: State=[-0.26900366 -0.00443923], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 45: State=[-0.27417204 -0.00516838], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 46: State=[-0.28004137 -0.00586933], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 47: State=[-0.28657913 -0.00653775], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 48: State=[-0.29374847 -0.00716935], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 49: State=[-0.3015083  -0.00775982], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 50: State=[-0.30981326 -0.00830497], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 51: State=[-0.31861395 -0.00880068], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 52: State=[-0.32785693 -0.00924298], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 53: State=[-0.33748505 -0.00962811], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 54: State=[-0.34743762 -0.00995256], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 55: State=[-0.35765073 -0.01021313], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 56: State=[-0.3680577  -0.01040696], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 57: State=[-0.37858933 -0.01053164], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 58: State=[-0.38917455 -0.01058523], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 59: State=[-0.39974087 -0.01056631], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 60: State=[-0.41021487 -0.01047401], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 61: State=[-0.42052296 -0.01030809], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 62: State=[-0.43059185 -0.01006889], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 63: State=[-0.44034928 -0.00975743], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 64: State=[-0.4497246  -0.00937533], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 65: State=[-0.4586495  -0.00892486], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 66: State=[-0.4670584  -0.00840891], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 67: State=[-0.4748893  -0.00783093], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 68: State=[-0.48208427 -0.00719495], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 69: State=[-0.48858976 -0.0065055 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 70: State=[-0.49435735 -0.00576759], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 71: State=[-0.499344   -0.00498662], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 72: State=[-0.5035123  -0.00416837], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 73: State=[-0.5068313  -0.00331893], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 74: State=[-0.5092759  -0.00244463], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 75: State=[-0.5108279  -0.00155202], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 76: State=[-0.5114757  -0.00064778], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 77: State=[-5.1121438e-01  2.6132065e-04], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 78: State=[-0.51004595  0.00116846], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 79: State=[-0.5079791   0.00206684], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 80: State=[-0.5050293   0.00294974], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 81: State=[-0.5012188   0.00381054], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 82: State=[-0.49657598  0.00464281], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 83: State=[-0.49113563  0.00544036], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 84: State=[-0.48493835  0.00619728], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 85: State=[-0.47803038  0.00690797], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 86: State=[-0.4704631   0.00756727], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 87: State=[-0.46229267  0.00817044], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 88: State=[-0.45357943  0.00871323], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 89: State=[-0.4443875   0.00919194], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 90: State=[-0.43478405  0.00960343], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 91: State=[-0.4248389   0.00994517], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 92: State=[-0.41462362  0.01021526], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 93: State=[-0.4042112   0.01041243], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 94: State=[-0.39367515  0.01053605], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 95: State=[-0.38308904  0.01058611], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 96: State=[-0.3725258   0.01056322], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 97: State=[-0.36205727  0.01046856], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 98: State=[-0.35175338  0.01030387], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 99: State=[-0.34168202  0.01007137], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 100: State=[-0.33190832  0.00977372], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 101: State=[-0.32249433  0.00941398], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 102: State=[-0.31349877  0.00899555], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 103: State=[-0.3049767   0.00852208], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 104: State=[-0.29697922  0.00799746], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 105: State=[-0.28955346  0.00742576], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 106: State=[-0.28274235  0.00681113], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 107: State=[-0.2765845   0.00615785], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 108: State=[-0.2711143  0.0054702], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 109: State=[-0.26636177  0.00475252], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 110: State=[-0.26235268  0.00400911], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 111: State=[-0.2591084   0.00324428], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 112: State=[-0.25664607  0.0024623 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 113: State=[-0.25497866  0.00166742], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 114: State=[-0.2541148   0.00086385], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 115: State=[-2.5405902e-01  5.5803481e-05], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 116: State=[-0.25481156 -0.00075253], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 117: State=[-0.25636852 -0.00155697], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 118: State=[-0.2587218 -0.0023533], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 119: State=[-0.26185912 -0.00313731], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 120: State=[-0.26576388 -0.00390476], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 121: State=[-0.27041528 -0.00465138], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 122: State=[-0.27578813 -0.00537287], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 123: State=[-0.28185305 -0.00606492], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 124: State=[-0.28857625 -0.0067232 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 125: State=[-0.29591966 -0.00734341], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 126: State=[-0.30384094 -0.00792129], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 127: State=[-0.3122936  -0.00845265], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 128: State=[-0.321227   -0.00893341], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 129: State=[-0.33058667 -0.00935966], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 130: State=[-0.34031436 -0.0097277 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 131: State=[-0.35034847 -0.01003411], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 132: State=[-0.36062425 -0.01027577], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 133: State=[-0.3710742  -0.01044996], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 134: State=[-0.3816286  -0.01055439], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 135: State=[-0.39221585 -0.01058726], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 136: State=[-0.4027632  -0.01054732], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 137: State=[-0.41319704 -0.01043387], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 138: State=[-0.42344388 -0.01024683], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 139: State=[-0.4334306  -0.00998674], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 140: State=[-0.4430854  -0.00965478], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 141: State=[-0.45233816 -0.00925278], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 142: State=[-0.46112132 -0.00878317], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 143: State=[-0.46937034 -0.00824901], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 144: State=[-0.4770243  -0.00765393], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 145: State=[-0.48402637 -0.00700211], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 146: State=[-0.4903246 -0.0062982], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 147: State=[-0.49587193 -0.00554735], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 148: State=[-0.500627   -0.00475506], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 149: State=[-0.5045542  -0.00392721], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 150: State=[-0.50762415 -0.00306997], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 151: State=[-0.5098139  -0.00218973], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 152: State=[-0.51110697 -0.00129309], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 153: State=[-5.1149374e-01 -3.8675268e-04], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 154: State=[-0.51097125  0.00052248], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 155: State=[-0.5095435  0.0014278], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 156: State=[-0.50722104  0.00232241], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 157: State=[-0.5040214   0.00319963], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 158: State=[-0.49996853  0.00405288], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 159: State=[-0.49509272  0.0048758 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 160: State=[-0.48943046  0.00566227], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 161: State=[-0.483024    0.00640646], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 162: State=[-0.47592112  0.0071029 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 163: State=[-0.46817458  0.00774653], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 164: State=[-0.45984182  0.00833276], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 165: State=[-0.45098433  0.0088575 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 166: State=[-0.44166714  0.00931718], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 167: State=[-0.43195826  0.00970887], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 168: State=[-0.42192808  0.01003019], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 169: State=[-0.41164866  0.01027943], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 170: State=[-0.40119317  0.0104555 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 171: State=[-0.39063522  0.01055795], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 172: State=[-0.38004825  0.01058696], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 173: State=[-0.36950496  0.0105433 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 174: State=[-0.35907665  0.01042832], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 175: State=[-0.34883273  0.0102439 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 176: State=[-0.33884037  0.00999238], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 177: State=[-0.3291638   0.00967656], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 178: State=[-0.31986418  0.0092996 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 179: State=[-0.3109992   0.00886497], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 180: State=[-0.30262282  0.0083764 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 181: State=[-0.294785    0.00783783], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 182: State=[-0.28753164  0.00725336], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 183: State=[-0.28090447  0.00662719], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 184: State=[-0.27494088  0.00596359], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 185: State=[-0.269674    0.00526687], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 186: State=[-0.26513267  0.00454135], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 187: State=[-0.2613413   0.00379135], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 188: State=[-0.25832015  0.00302116], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 189: State=[-0.25608513  0.00223503], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 190: State=[-0.25464788  0.00143722], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 191: State=[-0.25401595  0.00063194], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 192: State=[-2.5419256e-01 -1.7661984e-04], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 193: State=[-0.25517684 -0.00098426], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 194: State=[-0.25696364 -0.0017868 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 195: State=[-0.25954366 -0.00258003], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 196: State=[-0.2629034  -0.00335972], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 197: State=[-0.267025   -0.00412162], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 198: State=[-0.27188647 -0.00486146], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 199: State=[-0.27746138 -0.00557493], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Moviepy - Building video /content/videos/rl-video-episode-0.mp4.\n",
            "Moviepy - Writing video /content/videos/rl-video-episode-0.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/videos/rl-video-episode-0.mp4\n",
            "Step 200: State=[-0.2837191  -0.00625771], Reward=-1.0, term=False, trunc=True, info={}\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <video width=\"640\" height=\"480\" controls>\n",
              "        <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
              "    </video>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import gym\n",
        "import gymnasium as gymnasium\n",
        "from gymnasium.wrappers import RecordVideo\n",
        "import glob\n",
        "import io\n",
        "import base64\n",
        "from IPython.display import HTML\n",
        "from IPython import display as ipythondisplay\n",
        "from pyvirtualdisplay import Display\n",
        "import numpy as np\n",
        "\n",
        "# Start virtual display\n",
        "display = Display(visible=0, size=(1400, 900))\n",
        "display.start()\n",
        "\n",
        "# Create Mountain Car environment\n",
        "env = gymnasium.make('MountainCar-v0', render_mode=\"rgb_array\")\n",
        "env.metadata['render_fps'] = 30\n",
        "# Reset the environment\n",
        "env.reset()\n",
        "\n",
        "# Setup the wrapper to record the video\n",
        "video_callable=lambda episode_id: True\n",
        "env = RecordVideo(env, video_folder='./videos', episode_trigger=video_callable)\n",
        "\n",
        "# Run the environment until done\n",
        "\n",
        "truncated = False\n",
        "i=0\n",
        "while not truncated:\n",
        "  i+=1\n",
        "  action = 2\n",
        "  state, reward, terminated, truncated , info = env.step(action)\n",
        "  print(f\"Step {i}: State={state}, Reward={reward}, term={terminated}, trunc={truncated}, info={info}\")\n",
        "\n",
        "env.close()\n",
        "\n",
        "# Display the video\n",
        "video = io.open(glob.glob('videos/*.mp4')[0], 'r+b').read()\n",
        "encoded = base64.b64encode(video)\n",
        "ipythondisplay.display(HTML(data='''\n",
        "    <video width=\"640\" height=\"480\" controls>\n",
        "        <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
        "    </video>\n",
        "'''.format(encoded.decode('ascii'))))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVcdwI-0-Nod"
      },
      "source": [
        "## Programmed Car\n",
        "\n",
        "Now we will look at a car that I hand-programmed. This car is straightforward; however, it solves the problem. The programmed car always applies force in one direction or another. It does not break. Whatever direction the vehicle is currently rolling, the agent uses power in that direction. Therefore, the car begins to climb a hill, is overpowered, and turns backward. However, once it starts to roll backward, force is immediately applied in this new direction.\n",
        "\n",
        "The following code implements this preprogrammed car."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AMzYrqRy-Noe",
        "outputId": "5d1d4898-e0b4-481e-a9a8-f00af827f3b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/record_video.py:94: UserWarning: \u001b[33mWARN: Overwriting existing videos at /content/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
            "  logger.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Step 1: State=[-0.5504664   0.00121034], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 2: State=[-0.5480548   0.00241163], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 3: State=[-0.54445994  0.00359488], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 4: State=[-0.5397087   0.00475124], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 5: State=[-0.53383666  0.00587201], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 6: State=[-0.5268879   0.00694879], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 7: State=[-0.5189144   0.00797345], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 8: State=[-0.5099761   0.00893832], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 9: State=[-0.50013995  0.00983618], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 10: State=[-0.48947954  0.01066039], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 11: State=[-0.4780746   0.01140494], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 12: State=[-0.46601003  0.01206457], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 13: State=[-0.45337525  0.0126348 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 14: State=[-0.44026324  0.01311201], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 15: State=[-0.42676973  0.01349348], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 16: State=[-0.4129923   0.01377743], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 17: State=[-0.39902928  0.01396303], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 18: State=[-0.38497895  0.01405035], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 19: State=[-0.37093854  0.01404041], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 20: State=[-0.35700348  0.01393506], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 21: State=[-0.3432665   0.01373697], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 22: State=[-0.329817    0.01344949], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 23: State=[-0.3167404   0.01307662], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 24: State=[-0.30411753  0.01262286], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 25: State=[-0.29202437  0.01209315], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 26: State=[-0.28053167  0.01149271], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 27: State=[-0.26970464  0.01082703], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 28: State=[-0.25960296  0.01010168], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 29: State=[-0.25028065  0.0093223 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 30: State=[-0.24178614  0.00849451], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 31: State=[-0.23416229  0.00762386], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 32: State=[-0.22744651  0.00671577], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 33: State=[-0.221671    0.00577552], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 34: State=[-0.21686275  0.00480825], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 35: State=[-0.21304382  0.00381893], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 36: State=[-0.21023144  0.00281239], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 37: State=[-0.2084381   0.00179335], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 38: State=[-0.20767169  0.0007664 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 39: State=[-0.2079356  -0.00026391], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 40: State=[-0.21122867 -0.00329306], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 41: State=[-0.21753636 -0.00630769], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 42: State=[-0.2268303  -0.00929394], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 43: State=[-0.2390674 -0.0122371], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 44: State=[-0.25418863 -0.01512122], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 45: State=[-0.2721175  -0.01792889], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 46: State=[-0.2927586 -0.0206411], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 47: State=[-0.3159959  -0.02323729], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 48: State=[-0.3416915  -0.02569559], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 49: State=[-0.36968467 -0.02799318], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 50: State=[-0.39979163 -0.03010695], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 51: State=[-0.4318059 -0.0320143], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 52: State=[-0.4655     -0.03369408], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 53: State=[-0.50062764 -0.03512762], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 54: State=[-0.5369274  -0.03629977], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 55: State=[-0.5741272  -0.03719983], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 56: State=[-0.6119495  -0.03782231], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 57: State=[-0.6501169  -0.03816741], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 58: State=[-0.68835807 -0.03824115], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 59: State=[-0.72641325 -0.03805516], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 60: State=[-0.7640394  -0.03762618], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 61: State=[-0.80101466 -0.03697523], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 62: State=[-0.8371413  -0.03612662], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 63: State=[-0.87224805 -0.0351068 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 64: State=[-0.90619135 -0.0339433 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 65: State=[-0.93885505 -0.03266367], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 66: State=[-0.9701496  -0.03129456], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 67: State=[-1.0000106  -0.02986105], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 68: State=[-1.0283967  -0.02838605], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 69: State=[-1.0552868  -0.02689003], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 70: State=[-1.0806775  -0.02539077], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 71: State=[-1.1045809  -0.02390337], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 72: State=[-1.1270212  -0.02244032], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 73: State=[-1.1480328  -0.02101166], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 74: State=[-1.167658   -0.01962518], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 75: State=[-1.1859447  -0.01828665], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 76: State=[-1.2  0. ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 77: State=[-1.1987581  0.0012419], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 78: State=[-1.1942703  0.0044879], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 79: State=[-1.1865218   0.00774848], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 80: State=[-1.1754885   0.01103326], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 81: State=[-1.161138    0.01435038], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 82: State=[-1.1434324   0.01770574], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 83: State=[-1.1223301   0.02110228], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 84: State=[-1.0977911   0.02453904], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 85: State=[-1.0697807  0.0280103], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 86: State=[-1.0382762   0.03150456], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 87: State=[-1.0032725   0.03500367], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 88: State=[-0.9647905   0.03848199], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 89: State=[-0.9228845   0.04190598], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 90: State=[-0.8776504   0.04523414], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 91: State=[-0.82923275  0.04841765], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 92: State=[-0.7778308   0.05140195], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 93: State=[-0.72370166  0.05412914], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 94: State=[-0.6671603   0.05654138], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 95: State=[-0.60857517  0.05858511], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 96: State=[-0.5483596   0.06021556], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 97: State=[-0.4869585   0.06140109], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 98: State=[-0.42483166  0.06212684], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 99: State=[-0.36243477  0.06239688], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 100: State=[-0.30020007  0.0622347 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 101: State=[-0.23851824  0.06168184], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 102: State=[-0.17772321  0.06079502], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 103: State=[-0.1180812   0.05964202], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 104: State=[-0.05978395  0.05829725], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 105: State=[-0.0029466   0.05683735], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 106: State=[0.05239085 0.05533745], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 107: State=[0.10625911 0.05386826], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 108: State=[0.15875332 0.05249421], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 109: State=[0.21002574 0.05127243], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 110: State=[0.26027822 0.05025247], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 111: State=[0.30975488 0.04947665], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 112: State=[0.35873547 0.0489806 ], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 113: State=[0.40752938 0.04879392], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Step 114: State=[0.45647028 0.04894088], Reward=-1.0, term=False, trunc=False, info={}\n",
            "Moviepy - Building video /content/videos/rl-video-episode-0.mp4.\n",
            "Moviepy - Writing video /content/videos/rl-video-episode-0.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/videos/rl-video-episode-0.mp4\n",
            "Step 115: State=[0.5059111  0.04944082], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 116: State=[0.5562193  0.05030822], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 117: State=[0.6        0.05155249], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 118: State=[0.6        0.05312049], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 119: State=[0.6        0.05468849], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 120: State=[0.6       0.0562565], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 121: State=[0.6       0.0578245], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 122: State=[0.6        0.05939251], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 123: State=[0.6        0.06096052], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 124: State=[0.6        0.06252852], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 125: State=[0.6        0.06409653], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 126: State=[0.6        0.06566453], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 127: State=[0.6        0.06723253], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 128: State=[0.6        0.06880054], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 129: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 130: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 131: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 132: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 133: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 134: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 135: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 136: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 137: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 138: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 139: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 140: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 141: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 142: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 143: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 144: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 145: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 146: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 147: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 148: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 149: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 150: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 151: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 152: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 153: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 154: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 155: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 156: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 157: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 158: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 159: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 160: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 161: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 162: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 163: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 164: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 165: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 166: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 167: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 168: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 169: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 170: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 171: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 172: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 173: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 174: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 175: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 176: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 177: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 178: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 179: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 180: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 181: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 182: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 183: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 184: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 185: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 186: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 187: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 188: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 189: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 190: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 191: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 192: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 193: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 194: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 195: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 196: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 197: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 198: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 199: State=[0.6  0.07], Reward=-1.0, term=True, trunc=False, info={}\n",
            "Step 200: State=[0.6  0.07], Reward=-1.0, term=True, trunc=True, info={}\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <video width=\"640\" height=\"480\" controls>\n",
              "        <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
              "    </video>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import gym\n",
        "import gymnasium as gymnasium\n",
        "from gymnasium.wrappers import RecordVideo\n",
        "import glob\n",
        "import io\n",
        "import base64\n",
        "from IPython.display import HTML\n",
        "from IPython import display as ipythondisplay\n",
        "from pyvirtualdisplay import Display\n",
        "import numpy as np\n",
        "\n",
        "# Start virtual display\n",
        "display = Display(visible=0, size=(1400, 900))\n",
        "display.start()\n",
        "\n",
        "# Create Mountain Car environment\n",
        "env = gymnasium.make('MountainCar-v0', render_mode=\"rgb_array\")\n",
        "env.metadata['render_fps'] = 30\n",
        "# Reset the environment\n",
        "env.reset()\n",
        "\n",
        "# Setup the wrapper to record the video\n",
        "video_callable=lambda episode_id: True\n",
        "env = RecordVideo(env, video_folder='./videos', episode_trigger=video_callable)\n",
        "\n",
        "# Run the environment until done\n",
        "\n",
        "truncated = False\n",
        "i=0\n",
        "action = 2\n",
        "while not truncated:\n",
        "  i+=1\n",
        "  state, reward, terminated, truncated , info = env.step(action)\n",
        "  print(f\"Step {i}: State={state}, Reward={reward}, term={terminated}, trunc={truncated}, info={info}\")\n",
        "  if state[1] > 0:\n",
        "    action = 2\n",
        "  else:\n",
        "    action = 0\n",
        "\n",
        "env.close()\n",
        "\n",
        "# Display the video\n",
        "video = io.open(glob.glob('videos/*.mp4')[0], 'r+b').read()\n",
        "encoded = base64.b64encode(video)\n",
        "ipythondisplay.display(HTML(data='''\n",
        "    <video width=\"640\" height=\"480\" controls>\n",
        "        <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
        "    </video>\n",
        "'''.format(encoded.decode('ascii'))))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAyqqoy9-Nog"
      },
      "source": [
        "### Reinforcement Learning\n",
        "\n",
        "Q-Learning is a system of rewards that the algorithm gives an agent for successfully moving the environment into a state considered successful. These rewards are the Q-values from which this algorithm takes its name. The final output from the Q-Learning algorithm is a table of Q-values that indicate the reward value of every action that the agent can take, given every possible environment state. The agent must bin continuous state values into a fixed finite number of columns.\n",
        "\n",
        "Learning occurs when the algorithm runs the agent and environment through episodes and updates the Q-values based on the rewards received from actions taken; Figure 12.REINF provides a high-level overview of this reinforcement or Q-Learning loop.\n",
        "\n",
        "**Figure 12.REINF:Reinforcement/Q Learning**\n",
        "![Reinforcement Learning](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/reinforcement.png \"Reinforcement Learning\")\n",
        "\n",
        "The Q-values can dictate action by selecting the action column with the highest Q-value for the current environment state. The choice between choosing a random action and a Q-value-driven action is governed by the epsilon ($\\epsilon$) parameter, the probability of random action.\n",
        "\n",
        "Each time through the training loop, the training algorithm updates the Q-values according to the following equation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzZnd9Tj7rNc"
      },
      "source": [
        "$Q^{new}(s_{t},a_{t}) \\leftarrow \\underbrace{Q(s_{t},a_{t})}_{\\text{old value}} + \\underbrace{\\alpha}_{\\text{learning rate}} \\cdot  \\overbrace{\\bigg( \\underbrace{\\underbrace{r_{t}}_{\\text{reward}} + \\underbrace{\\gamma}_{\\text{discount factor}} \\cdot \\underbrace{\\max_{a}Q(s_{t+1}, a)}_{\\text{estimate of optimal future value}}}_{\\text{new value (temporal difference target)}} - \\underbrace{Q(s_{t},a_{t})}_{\\text{old value}} \\bigg) }^{\\text{temporal difference}}$\n",
        "\n",
        "There are several parameters in this equation:\n",
        "* alpha ($\\alpha$) - The learning rate, how much should the current step cause the Q-values to be updated.\n",
        "* lambda ($\\lambda$) - The discount factor is the percentage of future reward that the algorithm should consider in this update.\n",
        "\n",
        "This equation modifies several values:\n",
        "\n",
        "* $Q(s_t,a_t)$ - The Q-table.  For each combination of states, what reward would the agent likely receive for performing each action?\n",
        "* $s_t$ - The current state.\n",
        "* $r_t$ - The last reward received.\n",
        "* $a_t$ - The action that the agent will perform.\n",
        "\n",
        "The equation works by calculating a delta (temporal difference) that the equation should apply to the old state. This learning rate ($\\alpha$) scales this delta. A learning rate of 1.0 would fully implement the temporal difference in the Q-values each iteration and would likely be very chaotic.\n",
        "\n",
        "There are two parts to the temporal difference: the new and old values. The new value is subtracted from the old value to provide a delta; the full amount we would change the Q-value by if the learning rate did not scale this value. The new value is a summation of the reward received from the last action and the maximum Q-values from the resulting state when the client takes this action. Adding the maximum of action Q-values for the new state is essential because it estimates the optimal future values from proceeding with this action.\n",
        "\n",
        "## Q-Learning Car\n",
        "\n",
        "We will now use Q-Learning to produce a car that learns to drive itself. Look out, Tesla! We begin by defining two essential functions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrQgXqbS-Noi",
        "outputId": "68ef0118-a5eb-450c-c0a1-5666fe59fec6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "import gymnasium\n",
        "import numpy as np\n",
        "\n",
        "# This function converts the floating point state values into\n",
        "# discrete values. This is often called binning.  We divide\n",
        "# the range that the state values might occupy and assign\n",
        "# each region to a bucket.\n",
        "def calc_discrete_state(state):\n",
        "    discrete_state = (state - env.observation_space.low)/buckets\n",
        "    return tuple(discrete_state.astype(int))\n",
        "\n",
        "# Run one game.  The q_table to use is provided.  We also\n",
        "# provide a flag to indicate if the game should be\n",
        "# rendered/animated.  Finally, we also provide\n",
        "# a flag to indicate if the q_table should be updated.\n",
        "def run_game(q_table, render, should_update):\n",
        "    done = False\n",
        "    discrete_state = calc_discrete_state(env.reset()[0])\n",
        "    success = False\n",
        "\n",
        "    while not done:\n",
        "        # Exploit or explore\n",
        "        if np.random.random() > epsilon:\n",
        "            # Exploit - use q-table to take current best action\n",
        "            # (and probably refine)\n",
        "            action = np.argmax(q_table[discrete_state])\n",
        "        else:\n",
        "            # Explore - t\n",
        "            action = np.random.randint(0, env.action_space.n)\n",
        "\n",
        "        # Run simulation step\n",
        "        new_state, reward, done, _, _ = env.step(action)\n",
        "\n",
        "        # Convert continuous state to discrete\n",
        "        new_state_disc = calc_discrete_state(new_state)\n",
        "\n",
        "        # Have we reached the goal position (have we won?)?\n",
        "        if new_state[0] >= env.unwrapped.goal_position:\n",
        "            success = True\n",
        "\n",
        "        # Update q-table\n",
        "        if should_update:\n",
        "            max_future_q = np.max(q_table[new_state_disc])\n",
        "            current_q = q_table[discrete_state + (action,)]\n",
        "            new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * \\\n",
        "                (reward + DISCOUNT * max_future_q)\n",
        "            q_table[discrete_state + (action,)] = new_q\n",
        "\n",
        "        discrete_state = new_state_disc\n",
        "\n",
        "        if render:\n",
        "            env.render()\n",
        "\n",
        "    return success\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3j1M4MddIBF"
      },
      "source": [
        "Several hyperparameters are very important for Q-Learning. These parameters will likely need adjustment as you apply Q-Learning to other problems. Because of this, it is crucial to understand the role of each parameter.\n",
        "\n",
        "* **LEARNING_RATE** The rate at which previous Q-values are updated based on new episodes run during training.\n",
        "* **DISCOUNT** The amount of significance to give estimates of future rewards when added to the reward for the current action taken. A value of 0.95 would indicate a discount of 5% on the future reward estimates.\n",
        "* **EPISODES** The number of episodes to train over. Increase this for more complex problems; however, training time also increases.\n",
        "* **SHOW_EVERY** How many episodes to allow to elapse before showing an update.\n",
        "* **DISCRETE_GRID_SIZE** How many buckets to use when converting each continuous state variable. For example, [10, 10] indicates that the algorithm should use ten buckets for the first and second state variables.\n",
        "* **START_EPSILON_DECAYING** Epsilon is the probability that the agent will select a random action over what the Q-Table suggests. This value determines the starting probability of randomness.\n",
        "* **END_EPSILON_DECAYING** How many episodes should elapse before epsilon goes to zero and no random actions are permitted. For example, EPISODES//10  means only the first 1/10th of the episodes might have random actions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "rI0TJc7T-Nol"
      },
      "outputs": [],
      "source": [
        "LEARNING_RATE = 0.1\n",
        "DISCOUNT = 0.95\n",
        "EPISODES = 500\n",
        "SHOW_EVERY = 100\n",
        "\n",
        "DISCRETE_GRID_SIZE = [10, 10]\n",
        "START_EPSILON_DECAYING = 0.5\n",
        "END_EPSILON_DECAYING = EPISODES//10\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rw3SjzSyAktT"
      },
      "source": [
        "We can now make the environment.  If we are running in Google COLAB, we wrap the environment to be displayed inside the web browser.  Next, create the discrete buckets for state and build Q-table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2IdL-46y-Noo"
      },
      "outputs": [],
      "source": [
        "env = gymnasium.make(\"MountainCar-v0\")\n",
        "\n",
        "epsilon = 1\n",
        "epsilon_change = epsilon/(END_EPSILON_DECAYING - START_EPSILON_DECAYING)\n",
        "buckets = (env.observation_space.high - env.observation_space.low) \\\n",
        "    / DISCRETE_GRID_SIZE\n",
        "q_table = np.random.uniform(low=-3, high=0, size=(DISCRETE_GRID_SIZE\n",
        "                                                  + [env.action_space.n]))\n",
        "success = False\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2qG3ayDsRWz"
      },
      "source": [
        "We can now make the environment.  If we are running in Google COLAB, we wrap the environment to be displayed inside the web browser.  Next, create the discrete buckets for state and build Q-table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ot7cF0bX-Nor",
        "outputId": "b32cadb3-3f74-48fb-a5cf-2717e3e01648"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Current episode: 100, success: 99 0.99\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/envs/classic_control/mountain_car.py:173: UserWarning: \u001b[33mWARN: You are calling render method without specifying any render mode. You can specify the render_mode at initialization, e.g. gym.make(\"MountainCar-v0\", render_mode=\"rgb_array\")\u001b[0m\n",
            "  gym.logger.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Current episode: 200, success: 100 1.0\n",
            "Current episode: 300, success: 100 1.0\n",
            "Current episode: 400, success: 100 1.0\n",
            "Current episode: 500, success: 100 1.0\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "episode = 0\n",
        "success_count = 0\n",
        "\n",
        "# Loop through the required number of episodes\n",
        "while episode < EPISODES:\n",
        "    episode += 1\n",
        "    done = False\n",
        "\n",
        "    # Run the game.  If we are local, display render animation\n",
        "    # at SHOW_EVERY intervals.\n",
        "    if episode % SHOW_EVERY == 0:\n",
        "        print(f\"Current episode: {episode}, success: {success_count}\" +\n",
        "              f\" {(float(success_count)/SHOW_EVERY)}\")\n",
        "        success = run_game(q_table, True, False)\n",
        "        success_count = 0\n",
        "    else:\n",
        "        success = run_game(q_table, False, True)\n",
        "\n",
        "    # Count successes\n",
        "    if success:\n",
        "        success_count += 1\n",
        "\n",
        "    # Move epsilon towards its ending value, if it still needs to move\n",
        "    if END_EPSILON_DECAYING >= episode >= START_EPSILON_DECAYING:\n",
        "        epsilon = max(0, epsilon - epsilon_change)\n",
        "\n",
        "print(success)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDHTQkREFRSE"
      },
      "source": [
        "As you can see, the number of successful episodes generally increases as training progresses. It is not advisable to stop the first time we observe 100% success over 1,000 episodes. There is a randomness to most games, so it is not likely that an agent would retain its 100% success rate with a new run. It might be safe to stop training once you observe that the agent has gotten 100% for several update intervals.\n",
        "\n",
        "## Running and Observing the Agent\n",
        "\n",
        "Now that the algorithm has trained the agent, we can observe the agent in action. You can use the following code to see the agent in action."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "v_mf3A0h-Nox",
        "outputId": "c2df1204-12a9-4a3b-c60f-c68ea217c7c8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/record_video.py:94: UserWarning: \u001b[33mWARN: Overwriting existing videos at /content/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
            "  logger.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Building video /content/videos/rl-video-episode-0.mp4.\n",
            "Moviepy - Writing video /content/videos/rl-video-episode-0.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/videos/rl-video-episode-0.mp4\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <video width=\"640\" height=\"480\" controls>\n",
              "        <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
              "    </video>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# HIDE OUTPUT\n",
        "\n",
        "# Setup the wrapper to record the video\n",
        "env = gymnasium.make(\"MountainCar-v0\", render_mode=\"rgb_array\")\n",
        "video_callable=lambda episode_id: True\n",
        "env = RecordVideo(env, video_folder='./videos', episode_trigger=video_callable)\n",
        "\n",
        "run_game(q_table, True, False)\n",
        "\n",
        "# Display the video\n",
        "video = io.open(glob.glob('videos/*.mp4')[0], 'r+b').read()\n",
        "encoded = base64.b64encode(video)\n",
        "ipythondisplay.display(HTML(data='''\n",
        "    <video width=\"640\" height=\"480\" controls>\n",
        "        <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
        "    </video>\n",
        "'''.format(encoded.decode('ascii'))))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WeQzNFSosdys"
      },
      "source": [
        "## Inspecting the Q-Table\n",
        "\n",
        "We can also display the Q-table. The following code shows the agent's action for each environment state. As the weights of a neural network, this table is not straightforward to interpret. Some patterns do emerge in that direction, as seen by calculating the means of rows and columns. The actions seem consistent at both velocity and position's upper and lower halves."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "L62lyCTr-Noz",
        "outputId": "4be0cc48-8686-4bae-ec6b-0064cc950352"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c5ed7159-0326-4130-b4bf-46b068300bfd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>v-0</th>\n",
              "      <th>v-1</th>\n",
              "      <th>v-2</th>\n",
              "      <th>v-3</th>\n",
              "      <th>v-4</th>\n",
              "      <th>v-5</th>\n",
              "      <th>v-6</th>\n",
              "      <th>v-7</th>\n",
              "      <th>v-8</th>\n",
              "      <th>v-9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>p-0</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-6</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-7</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-8</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p-9</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5ed7159-0326-4130-b4bf-46b068300bfd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c5ed7159-0326-4130-b4bf-46b068300bfd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c5ed7159-0326-4130-b4bf-46b068300bfd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-213daca6-83e5-4fb1-a036-32c360e2d0ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-213daca6-83e5-4fb1-a036-32c360e2d0ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-213daca6-83e5-4fb1-a036-32c360e2d0ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_23fa1b5e-21d2-421a-9167-2574ce9492b8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_23fa1b5e-21d2-421a-9167-2574ce9492b8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     v-0  v-1  v-2  v-3  v-4  v-5  v-6  v-7  v-8  v-9\n",
              "p-0    2    2    0    1    2    2    2    0    2    1\n",
              "p-1    2    0    0    2    0    0    0    2    2    0\n",
              "p-2    2    2    1    2    1    2    2    1    2    1\n",
              "p-3    1    0    1    0    2    2    2    1    2    1\n",
              "p-4    1    0    0    2    0    2    2    2    2    0\n",
              "p-5    1    1    0    0    1    2    1    2    2    0\n",
              "p-6    2    1    0    0    0    0    2    2    2    2\n",
              "p-7    0    2    2    1    0    2    2    1    1    0\n",
              "p-8    0    1    1    1    0    2    1    2    1    2\n",
              "p-9    0    2    0    1    1    1    2    1    0    2"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(q_table.argmax(axis=2))\n",
        "\n",
        "df.columns = [f'v-{x}' for x in range(DISCRETE_GRID_SIZE[0])]\n",
        "df.index = [f'p-{x}' for x in range(DISCRETE_GRID_SIZE[1])]\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-ZOL-d5Jbon",
        "outputId": "fa5c9181-8842-4be3-b531-765135223c24"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "v-0    1.1\n",
              "v-1    1.1\n",
              "v-2    0.5\n",
              "v-3    1.0\n",
              "v-4    0.7\n",
              "v-5    1.5\n",
              "v-6    1.6\n",
              "v-7    1.4\n",
              "v-8    1.6\n",
              "v-9    0.9\n",
              "dtype: float64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.mean(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOGA4HP9KHW3",
        "outputId": "948b21dc-b66e-44a3-a6f7-942f929a1f17"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "p-0    1.4\n",
              "p-1    0.8\n",
              "p-2    1.6\n",
              "p-3    1.2\n",
              "p-4    1.1\n",
              "p-5    1.0\n",
              "p-6    1.1\n",
              "p-7    1.1\n",
              "p-8    1.1\n",
              "p-9    1.0\n",
              "dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.mean(axis=1)"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9 (torch)",
      "language": "python",
      "name": "pytorch"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
        "instance",
        "_Feature"
      ],
      "window_display": false
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}