t81_558_class_12_3_pytorch_reinforce.ipynb

{
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/t81_558_class_12_3_pytorch_reinforce.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmDI-h7cI0tI"
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      "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": "lsaQlK8fFQqH"
      },
      "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": "cKOCZlhUgXVK"
      },
      "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": "Y0WC-M08zNNq",
        "outputId": "c17cd0dd-acb7-46d2-cea5-20fe3bf31a46"
      },
      "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": "markdown",
      "metadata": {
        "id": "1u9QVVsShC9X"
      },
      "source": [
        "# Part 12.3: Stable-Baselines Q-Learning in Gymnasium\n",
        "\n",
        "As we covered in the previous part, Q-Learning is a robust machine learning algorithm. Unfortunately, Q-Learning requires that the Q-table contain an entry for every possible state that the environment can take. Traditional Q-learning might be a good learning algorithm if the environment only includes a handful of discrete state elements. However, the Q-table can become prohibitively large if the state space is large.\n",
        "\n",
        "Creating policies for large state spaces is a task that Deep Q-Learning Networks (DQN) can usually handle. Neural networks can generalize these states and learn commonalities. Unlike a table, a neural network does not require the program to represent every combination of state and action. A DQN maps the state to its input neurons and the action Q-values to the output neurons. The DQN effectively becomes a function that accepts the state and suggests action by returning the expected reward for each possible action. Figure 12.DQL demonstrates the DQN structure and mapping between state and action.\n",
        "\n",
        "**Figure 12.DQL: Deep Q-Learning (DQL)**\n",
        "![Deep Q-Learning](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/deepqlearning.png \"Reinforcement Learning\")\n",
        "\n",
        "As this diagram illustrates, the environment state contains several elements. For the basic DQN, the state can be a mix of continuous and categorical/discrete values. For the DQN, the discrete state elements the program typically encoded as dummy variables. The actions should be discrete when your program implements a DQN. Other algorithms support continuous outputs, which we will discuss later in this chapter.\n",
        "\n",
        "In the landscape of deep learning, the [Stable Baselines 3](https://stable-baselines3.readthedocs.io/en/master/) library emerges as a torchbearer for reinforcement learning (RL) enthusiasts and researchers opting for PyTorch. As an evolution of the original Stable Baselines framework, this iteration has been meticulously reforged with the PyTorch backend, providing a suite of reliable, high-performance RL algorithms. It is designed for ease of use, offering a straightforward API that invites both novices and seasoned practitioners to implement, experiment with, and extend upon cutting-edge RL methods. With Stable Baselines 3, one can expect robust pre-trained models, customizable neural network architectures, and comprehensive documentation that empowers users to deploy RL solutions efficiently. Its compatibility with PyTorch means that it seamlessly integrates into the dynamic ecosystem of deep learning tools, allowing for rapid prototyping and research iteration. Whether the goal is to solve discrete control tasks or navigate the complexities of high-dimensional environments.\n",
        "\n",
        "## DQN and the Cart-Pole Problem\n",
        "\n",
        "Barto (1983) first described the cart-pole problem. [[Cite:barto1983neuronlike]](http://www.derongliu.org/adp/adp-cdrom/Barto1983.pdf) A cart is connected to a rigid hinged pole. The cart is free to move only in the vertical plane of the cart/track. The agent can apply an impulsive \"left\" or \"right\" force F of a fixed magnitude to the cart at discrete time intervals. The cart-pole environment simulates the physics behind keeping the pole reasonably upright position on the cart. The environment has four state variables:\n",
        "* $x$ The position of the cart on the track.\n",
        "* $\\theta$ The angle of the pole with the vertical\n",
        "* $\\dot{x}$ The cart velocity.\n",
        "* $\\dot{\\theta}$ The rate of change of the angle.\n",
        "\n",
        "The action space consists of discrete actions:\n",
        "* Apply force left\n",
        "* Apply force right\n",
        "\n",
        "First, we must install Stable Baselines.\n"
      ]
    },
    {
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      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "FreS1XOPzO9X",
        "outputId": "7fcf5f94-424f-42c6-aa49-452f7489de59"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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      "source": [
        "# HIDE OUTPUT\n",
        "if COLAB:\n",
        "  !pip install stable-baselines3[extra] gymnasium\n",
        "  !pip install gymnasium[accept-rom-license,atari]\n",
        "  !pip install pyvirtualdisplay\n",
        "  !sudo apt-get install -y xvfb python-opengl ffmpeg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJCgJnx3g0yY"
      },
      "source": [
        "### The Cartpole Environment\n",
        "In the Cartpole environment:\n",
        "\n",
        "-   `observation` is an array of 4 floats:\n",
        "    -   the position and velocity of the cart\n",
        "    -   the angular position and velocity of the pole\n",
        "-   `reward` is a scalar float value\n",
        "-   `action` is a scalar integer with only two possible values:\n",
        "    -   `0` — \"move left\"\n",
        "    -   `1` — \"move right\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2UGR5t_iZX-",
        "outputId": "925bbefb-e329-4d9e-f287-c336282176a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time step:\n",
            "(array([-0.03997597,  0.01010498,  0.04918804,  0.03170852], dtype=float32), {})\n",
            "Next time step:\n",
            "(array([-0.03977387,  0.2044883 ,  0.04982221, -0.24505836], dtype=float32), 1.0, False, False, {})\n"
          ]
        }
      ],
      "source": [
        "import gymnasium as gym\n",
        "\n",
        "# Create and initialize the MountainCar environment\n",
        "env = gym.make('CartPole-v1', render_mode=\"rgb_array\")\n",
        "\n",
        "time_step = env.reset()\n",
        "print('Time step:')\n",
        "print(time_step)\n",
        "\n",
        "action = 1\n",
        "\n",
        "next_time_step = env.step(action)\n",
        "print('Next time step:')\n",
        "print(next_time_step)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuUqXAVmecTU"
      },
      "source": [
        "We can also visualize this environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "JpgsdYthvwTo",
        "outputId": "bd78d5b9-1bcf-461f-c2bd-306145b74cc6"
      },
      "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": {
            "image/png": "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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=600x400>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from PIL import Image\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": "_snCVvq5Z8lJ"
      },
      "source": [
        "The goal is to move the above cart without causing the pole to fall over."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBc9lj9VWWtZ"
      },
      "source": [
        "## Training the agent\n",
        "\n",
        "We will make use of Stable-Baselines3 to train an agent for this environment. We will make use of the MlpPolicy, which makes use of a Multi-Layer Peceptron (MLP), which is another name for neural network.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBq91RRQf5Ha",
        "outputId": "abd174e5-3b67-4f36-a14a-8564041cbe80"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/dtypes.py:35: DeprecationWarning: ml_dtypes.float8_e4m3b11 is deprecated. Use ml_dtypes.float8_e4m3b11fnuz\n",
            "  from tensorflow.tsl.python.lib.core import pywrap_ml_dtypes\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using cuda device\n",
            "---------------------------------\n",
            "| rollout/           |          |\n",
            "|    ep_len_mean     | 21.7     |\n",
            "|    ep_rew_mean     | 21.7     |\n",
            "| time/              |          |\n",
            "|    fps             | 372      |\n",
            "|    iterations      | 1        |\n",
            "|    time_elapsed    | 5        |\n",
            "|    total_timesteps | 2048     |\n",
            "---------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 25.5        |\n",
            "|    ep_rew_mean          | 25.5        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 424         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 9           |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.009339783 |\n",
            "|    clip_fraction        | 0.108       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -0.686      |\n",
            "|    explained_variance   | -8.09e-05   |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | 7.31        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0154     |\n",
            "|    value_loss           | 52.5        |\n",
            "-----------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 34           |\n",
            "|    ep_rew_mean          | 34           |\n",
            "| time/                   |              |\n",
            "|    fps                  | 437          |\n",
            "|    iterations           | 3            |\n",
            "|    time_elapsed         | 14           |\n",
            "|    total_timesteps      | 6144         |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0098777935 |\n",
            "|    clip_fraction        | 0.0603       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -0.669       |\n",
            "|    explained_variance   | 0.0794       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 10.8         |\n",
            "|    n_updates            | 20           |\n",
            "|    policy_gradient_loss | -0.0154      |\n",
            "|    value_loss           | 33.1         |\n",
            "------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 45.2        |\n",
            "|    ep_rew_mean          | 45.2        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 448         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 18          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.009577734 |\n",
            "|    clip_fraction        | 0.0939      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -0.637      |\n",
            "|    explained_variance   | 0.272       |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | 19          |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0191     |\n",
            "|    value_loss           | 48.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 58.6        |\n",
            "|    ep_rew_mean          | 58.6        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 459         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 22          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.009585822 |\n",
            "|    clip_fraction        | 0.0729      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -0.607      |\n",
            "|    explained_variance   | 0.326       |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | 26.6        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0165     |\n",
            "|    value_loss           | 62.8        |\n",
            "-----------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/stable_baselines3/common/evaluation.py:67: UserWarning: Evaluation environment is not wrapped with a ``Monitor`` wrapper. This may result in reporting modified episode lengths and rewards, if other wrappers happen to modify these. Consider wrapping environment first with ``Monitor`` wrapper.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean reward: 358.2 +/- 124.27131607897294\n"
          ]
        }
      ],
      "source": [
        "import gymnasium as gym\n",
        "from stable_baselines3 import PPO\n",
        "from stable_baselines3.common.env_util import make_vec_env\n",
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "\n",
        "# Create the CartPole environment\n",
        "env = make_vec_env('CartPole-v1', n_envs=1)\n",
        "\n",
        "# Instantiate the agent\n",
        "model = PPO('MlpPolicy', env, verbose=1)\n",
        "\n",
        "# Train the agent\n",
        "model.learn(total_timesteps=10000)\n",
        "\n",
        "# Save the agent\n",
        "model.save(\"ppo_cartpole\")\n",
        "\n",
        "# Create a fresh environment for evaluation\n",
        "eval_env = gym.make('CartPole-v1')\n",
        "\n",
        "# Evaluate the agent\n",
        "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10)\n",
        "\n",
        "print(f\"Mean reward: {mean_reward} +/- {std_reward}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7-XpPP99Cy7"
      },
      "source": [
        "## Videos\n",
        "\n",
        "We can easily visulaize the cart pole ageint in a video."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "9Q9DHuLyf_B1",
        "outputId": "c2dde222-38cd-4caf-8f53-c2f35ae63af1"
      },
      "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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving video to /videos/ppo-cartpole-step-0-to-step-1500.mp4\n",
            "Moviepy - Building video /videos/ppo-cartpole-step-0-to-step-1500.mp4.\n",
            "Moviepy - Writing video /videos/ppo-cartpole-step-0-to-step-1500.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /videos/ppo-cartpole-step-0-to-step-1500.mp4\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<video alt=\"test\" autoplay loop controls style=\"height: 400px;\"><source 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type=\"video/mp4\" /></video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv\n",
        "import base64\n",
        "from IPython import display as ipythondisplay\n",
        "from pathlib import Path\n",
        "\n",
        "# Record the agent playing\n",
        "video_folder = '/videos'\n",
        "video_length = 1500\n",
        "\n",
        "env = make_vec_env('CartPole-v1', n_envs=1)\n",
        "env = VecVideoRecorder(env, video_folder,\n",
        "                       record_video_trigger=lambda x: x == 0, video_length=video_length,\n",
        "                       name_prefix=\"ppo-cartpole\")\n",
        "\n",
        "obs = env.reset()\n",
        "for _ in range(video_length):\n",
        "    action, _ = model.predict(obs, deterministic=True)\n",
        "    obs, _, _, _ = env.step(action)\n",
        "\n",
        "# Close the environment and video recorder\n",
        "env.close()\n",
        "\n",
        "# Display the video\n",
        "video_path = Path(video_folder) / \"ppo-cartpole-step-0-to-step-1500.mp4\"\n",
        "video = open(video_path, \"rb\").read()\n",
        "encoded = base64.b64encode(video)\n",
        "\n",
        "ipythondisplay.display(ipythondisplay.HTML(data=f'<video alt=\"test\" autoplay loop controls style=\"height: 400px;\">'\n",
        "                                        f'<source src=\"data:video/mp4;base64,{encoded.decode()}\" type=\"video/mp4\" />'\n",
        "                                        f'</video>'))\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "anaconda-cloud": {},
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      "language": "python",
      "name": "pytorch"
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