Lunar-Lander-v2-PPO.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!apt install swig cmake"
      ],
      "metadata": {
        "id": "yQIGLPDkGhgG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "TODO CHANGE LINK OF THE REQUIREMENTS"
      ],
      "metadata": {
        "id": "32e3NPYgH5ET"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9XaULfDZDvrC"
      },
      "outputs": [],
      "source": [
        "!pip install -r https://huggingface.co/spaces/ThomasSimonini/temp-space-requirements/raw/main/requirements/requirements-unit1.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames). \n",
        "\n",
        "Hence the following cell will install virtual screen libraries and create and run a virtual screen 🖥"
      ],
      "metadata": {
        "id": "BEKeXQJsQCYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get update\n",
        "!apt install python-opengl\n",
        "!apt install ffmpeg\n",
        "!apt install xvfb\n",
        "!pip3 install pyvirtualdisplay"
      ],
      "metadata": {
        "id": "j5f2cGkdP-mb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make sure the new installed libraries are used, **sometimes it's required to restart the notebook runtime**. The next cell will force the **runtime to crash, so you'll need to connect again and run the code starting from here**. Thanks for this trick, **we will be able to run our virtual screen.**"
      ],
      "metadata": {
        "id": "TCwBTAwAW9JJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ],
      "metadata": {
        "id": "cYvkbef7XEMi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Virtual display\n",
        "from pyvirtualdisplay import Display\n",
        "\n",
        "virtual_display = Display(visible=0, size=(1400, 900))\n",
        "virtual_display.start()"
      ],
      "metadata": {
        "id": "BE5JWP5rQIKf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrgpVFqyENVf"
      },
      "source": [
        "## Import the packages 📦\n",
        "\n",
        "One additional library we import is huggingface_hub **to be able to upload and download trained models from the hub**.\n",
        "\n",
        "\n",
        "The Hugging Face Hub 🤗 works as a central place where anyone can share and explore models and datasets. It has versioning, metrics, visualizations and other features that will allow you to easily collaborate with others.\n",
        "\n",
        "You can see here all the Deep reinforcement Learning models available 👉 https://huggingface.co/models?pipeline_tag=reinforcement-learning&sort=downloads\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cygWLPGsEQ0m"
      },
      "outputs": [],
      "source": [
        "import gym\n",
        "\n",
        "from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub\n",
        "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
        "\n",
        "from stable_baselines3 import PPO\n",
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "from stable_baselines3.common.env_util import make_vec_env"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w7vOFlpA_ONz"
      },
      "outputs": [],
      "source": [
        "import gym\n",
        "\n",
        "# First, we create our environment called LunarLander-v2\n",
        "env = gym.make(\"LunarLander-v2\")\n",
        "\n",
        "# Then we reset this environment\n",
        "observation = env.reset()\n",
        "\n",
        "for _ in range(20):\n",
        "  # Take a random action\n",
        "  action = env.action_space.sample()\n",
        "  print(\"Action taken:\", action)\n",
        "\n",
        "  # Do this action in the environment and get\n",
        "  # next_state, reward, done and info\n",
        "  observation, reward, done, info = env.step(action)\n",
        "  \n",
        "  # If the game is done (in our case we land, crashed or timeout)\n",
        "  if done:\n",
        "      # Reset the environment\n",
        "      print(\"Environment is reset\")\n",
        "      observation = env.reset()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIrKGGSlENZB"
      },
      "source": [
        "## Create the LunarLander environment 🌛 and understand how it works\n",
        "### [The environment 🎮](https://www.gymlibrary.dev/environments/box2d/lunar_lander/)\n",
        "In this first tutorial, we’re going to train our agent, a [Lunar Lander](https://www.gymlibrary.dev/environments/box2d/lunar_lander/), **to land correctly on the moon**. To do that, the agent needs to learn **to adapt its speed and position(horizontal, vertical, and angular) to land correctly.**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "💡 A good habit when you start to use an environment is to check its documentation \n",
        "\n",
        "👉 https://www.gymlibrary.dev/environments/box2d/lunar_lander/\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poLBgRocF9aT"
      },
      "source": [
        "Let's see what the Environment looks like:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZNPG0g_UGCfh"
      },
      "outputs": [],
      "source": [
        "# We create our environment with gym.make(\"<name_of_the_environment>\")\n",
        "env = gym.make(\"LunarLander-v2\")\n",
        "env.reset()\n",
        "print(\"_____OBSERVATION SPACE_____ \\n\")\n",
        "print(\"Observation Space Shape\", env.observation_space.shape)\n",
        "print(\"Sample observation\", env.observation_space.sample()) # Get a random observation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MXc15qFE0M9"
      },
      "source": [
        "We see with `Observation Space Shape (8,)` that the observation is a vector of size 8, where each value contains different information about the lander:\n",
        "- Horizontal pad coordinate (x)\n",
        "- Vertical pad coordinate (y)\n",
        "- Horizontal speed (x)\n",
        "- Vertical speed (y)\n",
        "- Angle\n",
        "- Angular speed\n",
        "- If the left leg has contact point touched the land\n",
        "- If the right leg has contact point touched the land\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "We5WqOBGLoSm"
      },
      "outputs": [],
      "source": [
        "print(\"\\n _____ACTION SPACE_____ \\n\")\n",
        "print(\"Action Space Shape\", env.action_space.n)\n",
        "print(\"Action Space Sample\", env.action_space.sample()) # Take a random action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyxXwkI2Magx"
      },
      "source": [
        "The action space (the set of possible actions the agent can take) is discrete with 4 actions available 🎮: \n",
        "\n",
        "- Do nothing,\n",
        "- Fire left orientation engine,\n",
        "- Fire the main engine,\n",
        "- Fire right orientation engine.\n",
        "\n",
        "Reward function (the function that will gives a reward at each timestep) 💰:\n",
        "\n",
        "- Moving from the top of the screen to the landing pad and zero speed is about 100~140 points.\n",
        "- Firing main engine is -0.3 each frame\n",
        "- Each leg ground contact is +10 points\n",
        "- Episode finishes if the lander crashes (additional - 100 points) or come to rest (+100 points)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFD9RAFjG8aq"
      },
      "source": [
        "#### Vectorized Environment\n",
        "- We create a vectorized environment (method for stacking multiple independent environments into a single environment) of 16 environments, this way, **we'll have more diverse experiences during the training.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99hqQ_etEy1N"
      },
      "outputs": [],
      "source": [
        "# Create the environment\n",
        "env = make_vec_env('LunarLander-v2', n_envs=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgrE86r5E5IK"
      },
      "source": [
        "## Create the Model 🤖\n",
        "- Now that we studied our environment and we understood the problem: **being able to land correctly the Lunar Lander to the Landing Pad by controlling left, right and main orientation engine**. Let's build the algorithm we're going to use to solve this Problem 🚀.\n",
        "\n",
        "- To do so, we're going to use our first Deep RL library, [Stable Baselines3 (SB3)](https://stable-baselines3.readthedocs.io/en/master/).\n",
        "\n",
        "- SB3 is a set of **reliable implementations of reinforcement learning algorithms in PyTorch**.\n",
        "\n",
        "---\n",
        "\n",
        "💡 A good habit when using a new library is to dive first on the documentation: https://stable-baselines3.readthedocs.io/en/master/ and then try some tutorials.\n",
        "\n",
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HV4yiUM_9_Ka"
      },
      "source": [
        "To solve this problem, we're going to use SB3 **PPO**. [PPO (aka Proximal Policy Optimization) is one of the of the SOTA (state of the art) Deep Reinforcement Learning algorithms that you'll study during this course](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#example%5D).\n",
        "\n",
        "PPO is a combination of:\n",
        "- *Value-based reinforcement learning method*: learning an action-value function that will tell us what's the **most valuable action to take given a state and action**.\n",
        "- *Policy-based reinforcement learning method*: learning a policy that will **gives us a probability distribution over actions**.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qL_4HeIOrEJ"
      },
      "source": [
        "Stable-Baselines3 is easy to set up:\n",
        "\n",
        "1️⃣ You **create your environment** (in our case it was done above)\n",
        "\n",
        "2️⃣ You define the **model you want to use and instantiate this model** `model = PPO(\"MlpPolicy\")`\n",
        "\n",
        "3️⃣ You **train the agent** with `model.learn` and define the number of training timesteps\n",
        "\n",
        "```\n",
        "# Create environment\n",
        "env = gym.make('LunarLander-v2')\n",
        "\n",
        "# Instantiate the agent\n",
        "model = PPO('MlpPolicy', env, verbose=1)\n",
        "# Train the agent\n",
        "model.learn(total_timesteps=int(2e5))\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nxI6hT1GE4-A"
      },
      "outputs": [],
      "source": [
        "# TODO: Define a PPO MlpPolicy architecture\n",
        "# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector,\n",
        "# if we had frames as input we would use CnnPolicy\n",
        "model = PPO(\n",
        "    policy='MlpPolicy',\n",
        "    env=env, \n",
        "    n_steps=2048, # Bigger v1-2048\n",
        "    batch_size=64, #Bigger v1-64\n",
        "    learning_rate=0.0005, # Bigger LR\n",
        "    n_epochs=15,\n",
        "    ent_coef = 0.01, # v1-0.0\n",
        "    clip_range = 0.3,\n",
        "    verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAN7B0_HCVZC"
      },
      "source": [
        "#### Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "543OHYDfcjK4"
      },
      "outputs": [],
      "source": [
        "# SOLUTION\n",
        "# We added some parameters to accelerate the training\n",
        "model = PPO(\n",
        "    policy = 'MlpPolicy',\n",
        "    env = env,\n",
        "    n_steps = 2048,\n",
        "    batch_size = 128,\n",
        "    n_epochs = 4,\n",
        "    gamma = 0.999,\n",
        "    gae_lambda = 0.98,\n",
        "    ent_coef = 0.01,\n",
        "    verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClJJk88yoBUi"
      },
      "source": [
        "## Train the PPO agent 🏃\n",
        "- Let's train our agent for 1,000,000 timesteps, don't forget to use GPU on Colab. It will take approximately ~20min, but you can use less timesteps if you just want to try it out.\n",
        "- During the training, take a ☕ break you deserved it 🤗"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qKnYkNiVp89p"
      },
      "outputs": [],
      "source": [
        "# TODO: Specify file name for model and save the model to file\n",
        "model_name = \"ppo-lunal-lander-v2\"\n",
        "model.learn(\n",
        "    total_timesteps= 1000000*3, \n",
        "    progress_bar = True)\n",
        "model.save(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bQzQ-QcE3zo"
      },
      "source": [
        "#### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BY_HuedOoISR"
      },
      "source": [
        "## Evaluate the agent 📈\n",
        "- Now that our Lunar Lander agent is trained 🚀, we need to **check its performance**.\n",
        "- Stable-Baselines3 provides a method to do that: `evaluate_policy`.\n",
        "- To fill that part you need to [check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#basic-usage-training-saving-loading)\n",
        "- In the next step,  we'll see **how to automatically evaluate and share your agent to compete in a leaderboard, but for now let's do it ourselves**\n",
        "\n",
        "\n",
        "💡 When you evaluate your agent, you should not use your training environment but create an evaluation environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yRpno0glsADy"
      },
      "outputs": [],
      "source": [
        "# TODO: Evaluate the agent\n",
        "# model = PPO.load(\"ppo-lunal-lander-v2\")\n",
        "model_name = \"ppo-lunal-lander-v2\"\n",
        "model = PPO.load(model_name)\n",
        "# Create a new environment for evaluation\n",
        "eval_env = gym.make(\"LunarLander-v2\") \n",
        "\n",
        "# Evaluate the model with 10 evaluation episodes and deterministic=True\n",
        "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10)\n",
        "\n",
        "# Print the results\n",
        "print(f\"Mean reward is: {mean_reward}, std reward: {std_reward}\")\n",
        "# V1 results - 269.0829057792868, std reward: 18.585712707205865\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqPKw3jt_pG5"
      },
      "source": [
        "#### Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zpz8kHlt_a_m"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "eval_env = gym.make(\"LunarLander-v2\")\n",
        "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
        "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reBhoODwcXfr"
      },
      "source": [
        "- In my case, I got a mean reward is `200.20 +/- 20.80` after training for 1 million steps, which means that our lunar lander agent is ready to land on the moon 🌛🥳."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IK_kR78NoNb2"
      },
      "source": [
        "## Publish our trained model on the Hub 🔥\n",
        "Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code.\n",
        "\n",
        "📚 The libraries documentation 👉 https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face--x-stable-baselines3-v20\n",
        "\n",
        "Here's an example of a Model Card (with Space Invaders):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gs-Ew7e1gXN3"
      },
      "source": [
        "By using `package_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the hub**.\n",
        "\n",
        "This way:\n",
        "- You can **showcase our work** 🔥\n",
        "- You can **visualize your agent playing** 👀\n",
        "- You can **share with the community an agent that others can use** 💾\n",
        "- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZiFBBlzxzxY"
      },
      "outputs": [],
      "source": [
        "notebook_login()\n",
        "!git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tsf2uv0g_4p"
      },
      "source": [
        "If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGNh9VsZok0i"
      },
      "source": [
        "3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 using `package_to_hub()` function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ay24l6bqFF18"
      },
      "source": [
        "Let's fill the `package_to_hub` function:\n",
        "- `model`: our trained model.\n",
        "- `model_name`: the name of the trained model that we defined in `model_save`\n",
        "- `model_architecture`: the model architecture we used: in our case PPO\n",
        "- `env_id`: the name of the environment, in our case `LunarLander-v2`\n",
        "- `eval_env`: the evaluation environment defined in eval_env\n",
        "- `repo_id`: the name of the Hugging Face Hub Repository that will be created/updated `(repo_id = {username}/{repo_name})`\n",
        "\n",
        "💡 **A good name is {username}/{model_architecture}-{env_id}**\n",
        "\n",
        "- `commit_message`: message of the commit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JPG7ofdGIHN8"
      },
      "outputs": [],
      "source": [
        "import gym\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "from stable_baselines3.common.env_util import make_vec_env\n",
        "\n",
        "from huggingface_sb3 import package_to_hub\n",
        "\n",
        "## TODO: Define a repo_id\n",
        "## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2\n",
        "repo_id = \"Michunie/ppo-LunarLander-v2\"\n",
        "\n",
        "# TODO: Define the name of the environment\n",
        "env_id = \"LunarLander-v2\"\n",
        "\n",
        "# Create the evaluation env\n",
        "eval_env = DummyVecEnv([lambda: gym.make(env_id)])\n",
        "\n",
        "\n",
        "# TODO: Define the model architecture we used\n",
        "model_architecture = \"PPO\"\n",
        "\n",
        "## TODO: Define the commit message\n",
        "commit_message = \"Second try - changes parameters\"\n",
        "\n",
        "# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub\n",
        "package_to_hub(model=model, # Our trained model\n",
        "               model_name=model_name, # The name of our trained model \n",
        "               model_architecture=model_architecture, # The model architecture we used: in our case PPO\n",
        "               env_id=env_id, # Name of the environment\n",
        "               eval_env=eval_env, # Evaluation Environment\n",
        "               repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2\n",
        "               commit_message=commit_message)\n",
        "\n",
        "# Note: if after running the package_to_hub function and it gives an issue of rebasing, please run the following code\n",
        "# cd <path_to_repo> && git add . && git commit -m \"Add message\" && git pull \n",
        "# And don't forget to do a \"git push\" at the end to push the change to the hub."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nWnuQHRfFRa"
      },
      "source": [
        "## Load a saved LunarLander model from the Hub 🤗\n",
        "Thanks to [ironbar](https://github.com/ironbar) for the contribution.\n",
        "\n",
        "Loading a saved model from the Hub is really easy. \n",
        "\n",
        "You go https://huggingface.co/models?library=stable-baselines3 to see the list of all the Stable-baselines3 saved models.\n",
        "1. You select one and copy its repo_id\n",
        "\n",
        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit1/copy-id.png\" alt=\"Copy-id\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNPLJF2bfiUw"
      },
      "source": [
        "2. Then we just need to use load_from_hub with:\n",
        "- The repo_id\n",
        "- The filename: the saved model inside the repo and its extension (*.zip)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oj8PSGHJfwz3"
      },
      "outputs": [],
      "source": [
        "from huggingface_sb3 import load_from_hub\n",
        "repo_id = \"Classroom-workshop/assignment2-omar\" # The repo_id\n",
        "filename = \"ppo-LunarLander-v2.zip\" # The model filename.zip\n",
        "\n",
        "# When the model was trained on Python 3.8 the pickle protocol is 5\n",
        "# But Python 3.6, 3.7 use protocol 4\n",
        "# In order to get compatibility we need to:\n",
        "# 1. Install pickle5 (we done it at the beginning of the colab)\n",
        "# 2. Create a custom empty object we pass as parameter to PPO.load()\n",
        "custom_objects = {\n",
        "            \"learning_rate\": 0.0,\n",
        "            \"lr_schedule\": lambda _: 0.0,\n",
        "            \"clip_range\": lambda _: 0.0,\n",
        "}\n",
        "\n",
        "checkpoint = load_from_hub(repo_id, filename)\n",
        "model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fs0Y-qgPgLUf"
      },
      "source": [
        "Let's evaluate this agent:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "eval_env = gym.make(\"LunarLander-v2\")\n",
        "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
        "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
      ],
      "metadata": {
        "id": "PAEVwK-aahfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQAwLnYFPk-s"
      },
      "source": [
        "## Some additional challenges 🏆\n",
        "The best way to learn **is to try things by your own**! As you saw, the current agent is not doing great. As a first suggestion, you can train for more steps. With 1,000,000 steps, we saw some great results! \n",
        "\n",
        "In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?\n",
        "\n",
        "Here are some ideas to achieve so:\n",
        "* Train more steps\n",
        "* Try different hyperparameters of `PPO`. You can see them at https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#parameters. \n",
        "* Check the [Stable-Baselines3 documentation](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) and try another models such as DQN.\n",
        "* **Push your new trained model** on the Hub 🔥\n",
        "\n",
        "**Compare the results of your LunarLander-v2 with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) 🏆\n",
        "\n",
        "Is moon landing too boring to you? Try to **change the environment**, why not using MountainCar-v0, CartPole-v1 or CarRacing-v0? Check how they works [using the gym documentation](https://www.gymlibrary.dev/) and have fun 🎉."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lM95-dvmif8"
      },
      "source": [
        "________________________________________________________________________\n",
        "Congrats on finishing this chapter! That was the biggest one, **and there was a lot of information.**\n",
        "\n",
        "If you’re still feel confused with all these elements...it's totally normal! **This was the same for me and for all people who studied RL.**\n",
        "\n",
        "Take time to really **grasp the material before continuing and try the additional challenges**. It’s important to master these elements and having a solid foundations.\n",
        "\n",
        "Naturally, during the course, we’re going to use and deeper explain again these terms but **it’s better to have a good understanding of them now before diving into the next chapters.**\n"
      ]
    }
  ],
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    "gpuClass": "standard",
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