Lunar_Landing_Deep_Q_Learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hritik1100/Lunar-Lander-using-AI/blob/main/Lunar_Landing_Deep_Q_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbZcI9ZXHl3a"
      },
      "source": [
        "# Deep Q-Learning for Lunar Landing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8yPRjteXgPb"
      },
      "source": [
        "## Part 0 - Installing the required packages and importing the libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slEm5teGWjWU"
      },
      "source": [
        "### Installing Gymnasium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbnq3XpoKa_7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "7b49f203-345c-442d-ec99-6092baa28ef3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install gymnasium\n",
        "!pip install \"gymnasium[atari, accept-rom-license]\"\n",
        "!apt-get install -y swig\n",
        "!pip install gymnasium[box2d]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brqiMN3UW9T9"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mZaKXP_aMl9O"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.autograd as autograd\n",
        "from torch.autograd import Variable\n",
        "from collections import deque, namedtuple"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzlDKXvkXzGI"
      },
      "source": [
        "## Part 1 - Building the AI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtG6Zc83YYy3"
      },
      "source": [
        "### Creating the architecture of the Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Network(nn.Module):\n",
        "\n",
        "  def __init__(self, state_size, action_size, seed = 42):    ## 1st argument is Input Vector (Observation State) and 2nd argument is output vector (Action State)\n",
        "      super(Network, self).__init__()\n",
        "\n",
        "      self.seed = torch.manual_seed(seed)\n",
        "      self.fc1 = nn.Linear(state_size, 64)  ## Input to the Fully Connected Layer is the observation state and the output is 64 after doing trail and error\n",
        "      self.fc2 = nn.Linear(64, 64)          ## Created another FC layer with input as the output of the previous FC layer\n",
        "      self.fc3 = nn.Linear(64, action_size)\n",
        "\n",
        "\n",
        "  def forward(self, state):\n",
        "\n",
        "    ## 1st 2 lines: Propagate the signal from the input layer to the 1st fully connected layer with the relu activation function\n",
        "    x = self.fc1(state)\n",
        "    x = F.relu(x)\n",
        "\n",
        "    ## Next 2 lines: Does the same thing as above for the 2nd FC layer\n",
        "    x = self.fc2(x)\n",
        "    x = F.relu(x)\n",
        "\n",
        "    ## Take the output of 2nd FC layer and provide as input to the 3rd FC layer\n",
        "    return self.fc3(x)\n"
      ],
      "metadata": {
        "id": "1KcEWjfN9S0U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxVrBnFWZKb1"
      },
      "source": [
        "## Part 2 - Training the AI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T364fz9qZb2j"
      },
      "source": [
        "### Setting up the environment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculate observation space and action space of the environment\n",
        "\n",
        "import gymnasium as gym\n",
        "env = gym.make(\"LunarLander-v2\")\n",
        "state_shape = env.observation_space.shape\n",
        "state_size = env.observation_space.shape[0]\n",
        "\n",
        "number_actions = env.action_space.n\n",
        "\n",
        "print(\"State Shape: \", state_shape)\n",
        "print(\"State Size: \", state_size)\n",
        "print(\"Number of actions: \", number_actions)"
      ],
      "metadata": {
        "id": "BAUZmk_7z4Vq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adf23963-4650-4e29-bb10-8d5ecb8b947c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "State Shape:  (8,)\n",
            "State Size:  8\n",
            "Number of actions:  4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_dZmOIvZgj-"
      },
      "source": [
        "### Initializing the hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 5e-4              ##(5 * (10^-4))\n",
        "minibatch_size = 100              ## Batch size to update the model parameters\n",
        "discount_factor =  0.99           ## Gamma\n",
        "\n",
        "replay_buffer_size = int(1e5)     ## AI memory size\n",
        "interpolation_parameter = 1e-3    ## For soft update"
      ],
      "metadata": {
        "id": "137B9b8p1jm1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hD_Vs-bYnip"
      },
      "source": [
        "### Implementing Experience Replay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class ReplayMemory(object):\n",
        "\n",
        "  def __init__(self, capacity):\n",
        "    self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    self.capacity = capacity\n",
        "    self.memory = []   ## List which stores experiences each one containing action, state, reward, next state, and dones\n",
        "\n",
        "\n",
        "  def push(self, event):\n",
        "    self.memory.append(event)\n",
        "    if len(self.memory) > self.capacity:\n",
        "      del self.memory[0]    ## Delete the oldest memory which will be at the 0th index\n",
        "\n",
        "  def sample(self, batch_size):\n",
        "    experiences = random.sample(self.memory, k = batch_size)\n",
        "    states = torch.from_numpy(np.vstack([e[0] for e in experiences if e is not None])).float().to(self.device)\n",
        "    actions = torch.from_numpy(np.vstack([e[1] for e in experiences if e is not None])).long().to(self.device)\n",
        "    rewards = torch.from_numpy(np.vstack([e[2] for e in experiences if e is not None])).float().to(self.device)\n",
        "    next_states = torch.from_numpy(np.vstack([e[3] for e in experiences if e is not None])).float().to(self.device)\n",
        "    dones = torch.from_numpy(np.vstack([e[4] for e in experiences if e is not None]).astype(np.uint8)).float().to(self.device)\n",
        "\n",
        "    return states, next_states, actions, rewards, dones"
      ],
      "metadata": {
        "id": "1ebw4xRG4tmR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmEkbFbUY6Jt"
      },
      "source": [
        "### Implementing the DQN class"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent():\n",
        "\n",
        "  def __init__(self, state_size, action_size):\n",
        "    self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    self.state_size = state_size\n",
        "    self.action_size = action_size\n",
        "    self.local_qnetwork = Network(state_size, action_size).to(self.device)\n",
        "    self.target_qnetwork = Network(state_size, action_size).to(self.device)\n",
        "    self.optimizer = optim.Adam(self.local_qnetwork.parameters(), lr = learning_rate)\n",
        "    self.memory = ReplayMemory(replay_buffer_size)\n",
        "    self.t_step = 0\n",
        "\n",
        "  def step(self, state, action, reward, next_state, done):\n",
        "    self.memory.push((state, action, reward, next_state, done))\n",
        "    self.t_step = (self.t_step + 1) % 4\n",
        "    if self.t_step==0:\n",
        "      if len(self.memory.memory) > minibatch_size:\n",
        "        experiences = self.memory.sample(100)\n",
        "        self.learn(experiences, discount_factor)\n",
        "\n",
        "  def act(self, state, epsilon = 0.):\n",
        "    state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)\n",
        "    self.local_qnetwork.eval()\n",
        "    with torch.no_grad():\n",
        "      action_values = self.local_qnetwork(state)\n",
        "    self.local_qnetwork.train()\n",
        "    if random.random() > epsilon:\n",
        "      return np.argmax(action_values.cpu().data.numpy())\n",
        "    else:\n",
        "      return random.choice(np.arange(self.action_size))\n",
        "\n",
        "  def learn(self, experiences, discount_factor):\n",
        "    states, next_states, actions, rewards, dones = experiences\n",
        "    next_q_targets = self.target_qnetwork(next_states).detach().max(1)[0].unsqueeze(1)\n",
        "    q_targets = rewards + discount_factor * next_q_targets * (1 - dones)\n",
        "    q_expected = self.local_qnetwork(states).gather(1, actions)\n",
        "    loss = F.mse_loss(q_expected, q_targets)\n",
        "    self.optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    self.optimizer.step()\n",
        "    self.soft_update(self.local_qnetwork, self.target_qnetwork, interpolation_parameter)\n",
        "\n",
        "\n",
        "\n",
        "## Why we do soft update is to not abruptly change the target network parameters\n",
        "  def soft_update(self, local_model, target_model, interpolation_parameter):\n",
        "    for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n",
        "      target_param.data.copy_(interpolation_parameter * local_param.data + (1.0 - interpolation_parameter) * target_param.data)     ## Calculate weighted average of local and target parameters\n",
        "                                                                                                                                    ## to update target network parameters\n"
      ],
      "metadata": {
        "id": "nOo9HIgsCSBD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2a47681-7182-4b0c-e9d6-1f3609fcc3d5"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1tZElccZmf6"
      },
      "source": [
        "### Initializing the DQN agent"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent = Agent(state_size, number_actions)"
      ],
      "metadata": {
        "id": "yU58mIowWc3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8v0PtUfaVQp"
      },
      "source": [
        "### Training the DQN agent"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number_episodes = 2000\n",
        "maximum_number_timesteps_per_episode = 1000\n",
        "epsilon_starting_value = 1.0\n",
        "epsilon_ending_value = 0.01\n",
        "epsilon_decay_value = 0.995    ## This value should be close to 1\n",
        "epsilon = epsilon_starting_value\n",
        "scores_on_100_episodes = deque(maxlen = 100)\n",
        "\n",
        "for episode in range(number_episodes):\n",
        "  state, _ = env.reset()\n",
        "  score = 0\n",
        "  for t in range(maximum_number_timesteps_per_episode):\n",
        "    action = agent.act(state, epsilon)      ## Select action by making use of epsilon greedy approach\n",
        "    next_state, reward, done, _, _ = env.step(action)  ## Apply selected action to the current state to get next state and its associated rewards\n",
        "    agent.step(state, action, reward, next_state, done)    ## To make the agent learn through back propagation\n",
        "    state = next_state\n",
        "    score += reward                                       ## Reward is added which makes up to the total score\n",
        "    if done:\n",
        "      break\n",
        "\n",
        "  scores_on_100_episodes.append(score)\n",
        "  epsilon = max(epsilon_ending_value, epsilon_decay_value * epsilon)\n",
        "\n",
        "  print(\"\\rEpisode {}\\t Average Score: {:.2f}\".format(episode, np.mean(scores_on_100_episodes)), end = \"\")\n",
        "  if episode % 100 == 0:\n",
        "    print(\"\\rEpisode {}\\t Average Score: {:.2f}\".format(episode, np.mean(scores_on_100_episodes)))\n",
        "  if np.mean(scores_on_100_episodes) >= 200.0:\n",
        "    print(\"\\nEnvironment solved in {:d} episode!\\tAverage Score: {:.2f}\".format(episode - 100, np.mean(scores_on_100_episodes)), end = \"\")\n",
        "    torch.save(agent.local_qnetwork.state_dict(), \"checkpoint.pth\")\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "aCzl2bdqWhoE",
        "outputId": "1483e6bb-f4c5-40be-8ca5-300c9a4a4301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 0\t Average Score: -398.56\n",
            "Episode 100\t Average Score: -183.50\n",
            "Episode 200\t Average Score: -122.58\n",
            "Episode 300\t Average Score: -25.68\n",
            "Episode 400\t Average Score: -10.83\n",
            "Episode 500\t Average Score: 141.15\n",
            "Episode 591\t Average Score: 200.23\n",
            " Environment solved in 491 episode!\tAverage Score: 200.23"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O8CNwdOTcCoP"
      },
      "source": [
        "## Part 3 - Visualizing the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cb9nVvU2Okhk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "00a579c0-625c-4b25-f436-b99562a8c43e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:imageio_ffmpeg:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (600, 400) to (608, 400) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n",
            "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = _posixsubprocess.fork_exec(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video alt=\"test\" autoplay\n",
              "                loop controls style=\"height: 400px;\">\n",
              "                <source 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type=\"video/mp4\" />\n",
              "             </video>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import glob\n",
        "import io\n",
        "import base64\n",
        "import imageio\n",
        "from IPython.display import HTML, display\n",
        "from gym.wrappers.monitoring.video_recorder import VideoRecorder\n",
        "\n",
        "def show_video_of_model(agent, env_name):\n",
        "    env = gym.make(env_name, render_mode='rgb_array')\n",
        "    state, _ = env.reset()\n",
        "    done = False\n",
        "    frames = []\n",
        "    while not done:\n",
        "        frame = env.render()\n",
        "        frames.append(frame)\n",
        "        action = agent.act(state)\n",
        "        state, reward, done, _, _ = env.step(action.item())\n",
        "    env.close()\n",
        "    imageio.mimsave('video.mp4', frames, fps=30)\n",
        "\n",
        "show_video_of_model(agent, 'LunarLander-v2')\n",
        "\n",
        "def show_video():\n",
        "    mp4list = glob.glob('*.mp4')\n",
        "    if len(mp4list) > 0:\n",
        "        mp4 = mp4list[0]\n",
        "        video = io.open(mp4, 'r+b').read()\n",
        "        encoded = base64.b64encode(video)\n",
        "        display(HTML(data='''<video alt=\"test\" autoplay\n",
        "                loop controls style=\"height: 400px;\">\n",
        "                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
        "             </video>'''.format(encoded.decode('ascii'))))\n",
        "    else:\n",
        "        print(\"Could not find video\")\n",
        "\n",
        "show_video()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}