Mountain-Car-Gym-Q-Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyNHzQZAxWn5CAtCc9ao3nGA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/henriquebap/Machine_Learning_HB/blob/main/Mountain-Car-Gym-Q-Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mountain Car - OpenIA Gym\n",
        "---\n",
        "## Description\n",
        "- The Mountain Car MDP is a deterministic MDP that consists of a car placed stochastically at the bottom of a sinusoidal valley, with the only possible actions being the accelerations that can be applied to the car in either direction. The goal of the MDP is to strategically accelerate the car to reach the goal state on top of the right hill. There are two versions of the mountain car domain in gym: one with discrete actions and one with continuous. This version is the one with discrete actions.\n",
        "---\n",
        "## Observation Space\n",
        "- The observation is a `ndarray` with shape `(2,)` where the elements correspond to the following:\n",
        "\n",
        " ![ObservationSpace.jpg](data:image/jpeg;base64,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)\n",
        "\n",
        "##Action Space\n",
        "- There are 3 discrete deterministic actions:\n",
        "  ![Action Space.jpg](data:image/jpeg;base64,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)\n",
        "---\n",
        "# Transition Dynamics:\n",
        "### Given an action, the mountain car follows the following transition dynamics:\n",
        "\n",
        "- velocityt+1 = velocityt + (action - 1) * force - cos(3 * positiont) * gravity\n",
        "\n",
        "- positiont+1 = positiont + velocityt+1\n",
        "\n",
        "- where force = 0.001 and gravity = 0.0025. The collisions at either end are inelastic with the velocity set to 0 upon collision with the wall. The position is clipped to the range `[-1.2, 0.6]` and velocity is clipped to the range `[-0.07, 0.07]`.\n",
        "---\n",
        "# Reward:\n",
        "The goal is to reach the flag placed on top of the right hill as quickly as possible, as such the agent is penalised with a reward of -1 for each timestep.\n",
        "---\n",
        "# Gym Documentation\n",
        "[Link](https://www.gymlibrary.dev/environments/classic_control/mountain_car/)\n",
        "---\n",
        "# Aprendizado por reforco usando Q-Learning\n",
        "---\n",
        "## Conceitos basicos\n",
        "- Aprendizado por Reforço: É um tipo de aprendizado de máquina onde um agente aprende a tomar decisões em um ambiente, interagindo continuamente com ele. O agente recebe recompensas por suas ações e aprende ações que maximizam a recompensa ao longo do tempo.\n",
        "\n",
        "- Tabela Q (Q-table): No Q-learning, o agente mantém uma tabela (ou matriz) chamada Tabela Q, onde cada entrada Q[s, a] representa o valor esperado de obter uma recompensa ao realizar a ação 'a' em um estado 's'. O objetivo do agente é aprender a tabela Q que maximiza as recompensas cumulativas.\n",
        "---\n",
        "## Notebook\n",
        "- Notebook com uma solucao simples do projeto do Mountain car do ambiente da gym da Open IA\n",
        "- Projeto com o objetivo de estudos e desenolvimento de aprendizado\n",
        "---\n",
        "#Contato\n",
        "- **LinkedIn** : [Henrique Baptista](https://www.linkedin.com/in/henrique-baptista777/)\n",
        "- **GitHub** : [henriquebap](https://github.com/henriquebap)\n",
        "- **Email** : [henriquebaptista2003@gmail.com](mailto:henriquebaptista2003@gmail.com)"
      ],
      "metadata": {
        "id": "7ff4cjGpujQn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkFQh_vGZ3D5",
        "outputId": "32a21b41-07ed-4592-c6d3-3f7d0f02209e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gym in /usr/local/lib/python3.10/dist-packages (0.25.2)\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from gym) (1.25.2)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gym) (2.2.1)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from gym) (0.0.8)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "E: Unable to locate package python-opengl\n",
            "Requirement already satisfied: imageio[ffmpeg] in /usr/local/lib/python3.10/dist-packages (2.31.6)\n",
            "Collecting pyvirtualdisplay\n",
            "  Downloading PyVirtualDisplay-3.0-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imageio[ffmpeg]) (1.25.2)\n",
            "Requirement already satisfied: pillow<10.1.0,>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from imageio[ffmpeg]) (9.4.0)\n",
            "Requirement already satisfied: imageio-ffmpeg in /usr/local/lib/python3.10/dist-packages (from imageio[ffmpeg]) (0.4.9)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from imageio[ffmpeg]) (5.9.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from imageio-ffmpeg->imageio[ffmpeg]) (67.7.2)\n",
            "Installing collected packages: pyvirtualdisplay\n",
            "Successfully installed pyvirtualdisplay-3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install gym\n",
        "!apt-get install -y xvfb python-opengl ffmpeg\n",
        "!pip install imageio[ffmpeg] pyvirtualdisplay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "k8PKyHJLZ40J"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "env = gym.make('MountainCar-v0')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWOmmANJZ7WM",
        "outputId": "8cefada8-b266-41c6-f030-4fc41fdfc25c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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/gym/core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
            "  deprecation(\n",
            "/usr/local/lib/python3.10/dist-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
            "  deprecation(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurações do algoritmo Q-learning\n",
        "alpha = 0.5  # taxa de aprendizado\n",
        "gamma = 0.70  # fator de desconto\n",
        "epsilon = 0.5  # taxa de exploração\n",
        "\n",
        "# Discretização do espaço de estados\n",
        "num_bins = 20\n",
        "bins = [np.linspace(-1.2, 0.6, num_bins), np.linspace(-0.07, 0.07, num_bins)]\n",
        "\n",
        "# Inicialização da tabela Q com zeros\n",
        "Q = np.zeros((num_bins, num_bins, env.action_space.n))\n",
        "\n",
        "def discretize(state, bins):\n",
        "    return tuple(np.digitize(s, b) - 1 for s, b in zip(state, bins))"
      ],
      "metadata": {
        "id": "VfTNwqjVZ9NF"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_agent(env, Q, alpha, gamma, epsilon, bins, num_episodes=10000):\n",
        "    for episode in range(num_episodes):\n",
        "        state = env.reset()\n",
        "        state = discretize(state, bins)\n",
        "\n",
        "        done = False\n",
        "        while not done:\n",
        "            if np.random.random() < epsilon:\n",
        "                action = env.action_space.sample()  # Exploração\n",
        "            else:\n",
        "                action = np.argmax(Q[state])  # Exploração\n",
        "\n",
        "            next_state, reward, done, _ = env.step(action)\n",
        "            next_state = discretize(next_state, bins)\n",
        "\n",
        "            Q[state][action] = (1 - alpha) * Q[state][action] + alpha * (reward + gamma * np.max(Q[next_state]))\n",
        "            state = next_state\n",
        "\n",
        "        if episode % 1000 == 0:\n",
        "            print(f\"Episode {episode} completed\")"
      ],
      "metadata": {
        "id": "w3pdZdoxaBN2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_agent(env, Q, alpha, gamma, epsilon, bins)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yASY61aFaFtD",
        "outputId": "9d7654e9-f93b-4497-cdb8-2e6f17ad1926"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gym/utils/passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
            "  if not isinstance(terminated, (bool, np.bool8)):\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 0 completed\n",
            "Episode 1000 completed\n",
            "Episode 2000 completed\n",
            "Episode 3000 completed\n",
            "Episode 4000 completed\n",
            "Episode 5000 completed\n",
            "Episode 6000 completed\n",
            "Episode 7000 completed\n",
            "Episode 8000 completed\n",
            "Episode 9000 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_agent(env, Q, bins, render=False):\n",
        "    state = env.reset()\n",
        "    state = discretize(state, bins)\n",
        "    total_reward = 0\n",
        "    done = False\n",
        "    while not done:\n",
        "        action = np.argmax(Q[state])\n",
        "        state, reward, done, _ = env.step(action)\n",
        "        state = discretize(state, bins)\n",
        "        total_reward += reward\n",
        "        if render:\n",
        "            env.render()\n",
        "    return total_reward\n",
        "\n",
        "# Avaliação do agente\n",
        "reward = evaluate_agent(env, Q, bins, render=True)\n",
        "print(f\"Total Reward: {reward}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36sClB4paItR",
        "outputId": "464574eb-7098-41e1-f626-08e2ccfe884c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gym/core.py:49: DeprecationWarning: \u001b[33mWARN: You are calling render method, but you didn't specified the argument render_mode at environment initialization. To maintain backward compatibility, the environment will render in human mode.\n",
            "If you want to render in human mode, initialize the environment in this way: gym.make('EnvName', render_mode='human') and don't call the render method.\n",
            "See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
            "  deprecation(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Reward: -200.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rewards = [evaluate_agent(env, Q, bins) for _ in range(100)]\n",
        "plt.plot(rewards)\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Total Reward')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "iw-oiqytaKLC",
        "outputId": "307b4fcf-2c12-4646-f0b2-5e55def418dd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env.close()"
      ],
      "metadata": {
        "id": "dRxO_ZvdaN37"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}