From e0197c4e0e6556745fbfe110c07a86df4f90d387 Mon Sep 17 00:00:00 2001 From: simoninithomas Date: Mon, 5 Dec 2022 10:37:36 +0100 Subject: [PATCH] Put mdx notebook unit1 --- notebooks/unit1/unit1.ipynb | 2 +- notebooks/unit1/unit1.mdx | 617 +++++++++++++++++++++++++++++++++++ units/en/unit1/hands-on.mdx | 631 ++++++++++++++++++++++++++++++++++++ 3 files changed, 1249 insertions(+), 1 deletion(-) create mode 100644 notebooks/unit1/unit1.mdx diff --git a/notebooks/unit1/unit1.ipynb b/notebooks/unit1/unit1.ipynb index 58aea61..41287d2 100644 --- a/notebooks/unit1/unit1.ipynb +++ b/notebooks/unit1/unit1.ipynb @@ -1143,4 +1143,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/unit1/unit1.mdx b/notebooks/unit1/unit1.mdx new file mode 100644 index 0000000..341a95f --- /dev/null +++ b/notebooks/unit1/unit1.mdx @@ -0,0 +1,617 @@ +# Unit 1: Train your first Deep Reinforcement Learning Agent ๐Ÿค– +![Cover](https://github.com/huggingface/deep-rl-class/blob/main/unit1/assets/img/thumbnail.png?raw=true) + +In this notebook, you'll train your **first Deep Reinforcement Learning agent** a Lunar Lander agent that will learn to **land correctly on the Moon ๐ŸŒ•**. Using [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) a Deep Reinforcement Learning library, share them with the community, and experiment with different configurations + +โฌ‡๏ธ Here is an example of what **you will achieve in just a couple of minutes.** โฌ‡๏ธ + + + + +```python +%%html + +``` + +### The environment ๐ŸŽฎ +- [LunarLander-v2](https://www.gymlibrary.dev/environments/box2d/lunar_lander/) + +### The library used ๐Ÿ“š +- [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) + +We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues). + +## Objectives of this notebook ๐Ÿ† +At the end of the notebook, you will: +- Be able to use **Gym**, the environment library. +- Be able to use **Stable-Baselines3**, the deep reinforcement learning library. +- Be able to **push your trained agent to the Hub** with a nice video replay and an evaluation score ๐Ÿ”ฅ. + + + + +## This notebook is from Deep Reinforcement Learning Course +Deep RL Course illustration + +In this free course, you will: + +- ๐Ÿ“– Study Deep Reinforcement Learning in **theory and practice**. +- ๐Ÿง‘โ€๐Ÿ’ป Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0. +- ๐Ÿค– Train **agents in unique environments** + +And more check ๐Ÿ“š the syllabus ๐Ÿ‘‰ https://simoninithomas.github.io/deep-rl-course + +Donโ€™t forget to **sign up to the course** (we are collecting your email to be able toย **send you the links when each Unit is published and give you information about the challenges and updates).** + + +The best way to keep in touch and ask questions is to join our discord server to exchange with the community and with us ๐Ÿ‘‰๐Ÿป https://discord.gg/ydHrjt3WP5 + +## Prerequisites ๐Ÿ—๏ธ +Before diving into the notebook, you need to: + +๐Ÿ”ฒ ๐Ÿ“ **Done Unit 0** that gives you all the **information about the course and help you to onboard** ๐Ÿค— ADD LINK + +๐Ÿ”ฒ ๐Ÿ“š **Develop an understanding of the foundations of Reinforcement learning** (MC, TD, Rewards hypothesis...) by doing Unit 1 ๐Ÿ‘‰ ADD LINK + +## A small recap of what is Deep Reinforcement Learning ๐Ÿ“š +The RL process + +Let's do a small recap on what we learned in the first Unit: +- Reinforcement Learning is a **computational approach to learning from action**. We build an agent that learns from the environment by **interacting with it through trial and error** and receiving rewards (negative or positive) as feedback. + +- The goal of any RL agent is to **maximize its expected cumulative reward** (also called expected return) because RL is based on the _reward hypothesis_, which is that all goals can be described as the maximization of the expected cumulative reward. + +- The RL process is a **loop that outputs a sequence of state, action, reward, and next state**. + +- To calculate the expected cumulative reward (expected return), **we discount the rewards**: the rewards that come sooner (at the beginning of the game) are more probable to happen since they are more predictable than the long-term future reward. + +- To solve an RL problem, you want to **find an optimal policy**; the policy is the "brain" of your AI that will tell us what action to take given a state. The optimal one is the one that gives you the actions that max the expected return. + + +There are **two** ways to find your optimal policy: +- By **training your policy directly**: policy-based methods. +- By **training a value function** that tells us the expected return the agent will get at each state and use this function to define our policy: value-based methods. + +- Finally, we spoke about Deep RL because **we introduce deep neural networks to estimate the action to take (policy-based) or to estimate the value of a state (value-based) hence the name "deep."** + +# Let's train our first Deep Reinforcement Learning agent and upload it to the Hub ๐Ÿš€ + + +## Set the GPU ๐Ÿ’ช +- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type` + +GPU Step 1 + +- `Hardware Accelerator > GPU` + +GPU Step 2 + +## Install dependencies and create a virtual screen ๐Ÿ”ฝ +The first step is to install the dependencies, weโ€™ll install multiple ones. + +- `gym[box2D]`: Contains the LunarLander-v2 environment ๐ŸŒ› (we use `gym==0.21`) +- `stable-baselines3[extra]`: The deep reinforcement learning library. +- `huggingface_sb3`: Additional code for Stable-baselines3 to load and upload models from the Hugging Face ๐Ÿค— Hub. + +To make things easier, we created a script to install all these dependencies. + +```python +!apt install swig cmake +``` + +TODO CHANGE LINK OF THE REQUIREMENTS + +```python +!pip install -r https://huggingface.co/spaces/ThomasSimonini/temp-space-requirements/raw/main/requirements/requirements-unit1.txt +``` + +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). + +Hence the following cell will install virtual screen libraries and create and run a virtual screen ๐Ÿ–ฅ + +```python +!sudo apt-get update +!apt install python-opengl +!apt install ffmpeg +!apt install xvfb +!pip3 install pyvirtualdisplay +``` + +```python +# Virtual display +from pyvirtualdisplay import Display + +virtual_display = Display(visible=0, size=(1400, 900)) +virtual_display.start() +``` + +## Import the packages ๐Ÿ“ฆ + +One additional library we import is huggingface_hub **to be able to upload and download trained models from the hub**. + + +The Hugging Face Hub ๐Ÿค— 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. + +You can see here all the Deep reinforcement Learning models available ๐Ÿ‘‰ https://huggingface.co/models?pipeline_tag=reinforcement-learning&sort=downloads + + + +```python +import gym + +from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub +from huggingface_hub import ( + notebook_login, +) # To log to our Hugging Face account to be able to upload models to the Hub. + +from stable_baselines3 import PPO +from stable_baselines3.common.evaluation import evaluate_policy +from stable_baselines3.common.env_util import make_vec_env +``` + +## Understand what is Gym and how it works ๐Ÿค– + +๐Ÿ‹ The library containing our environment is called Gym. +**You'll use Gym a lot in Deep Reinforcement Learning.** + +The Gym library provides two things: +- An interface that allows you to **create RL environments**. +- A **collection of environments** (gym-control, atari, box2D...). + +Let's look at an example, but first let's remember what's the RL Loop. + +The RL process + +At each step: +- Our Agent receivesย **state S0**ย from theย **Environment**ย โ€” we receive the first frame of our game (Environment). +- Based on thatย **state S0,**ย the Agent takesย **action A0**ย โ€” our Agent will move to the right. +- Environment to aย **new**ย **state S1**ย โ€” new frame. +- The environment gives someย **reward R1**ย to the Agent โ€” weโ€™re not deadย *(Positive Reward +1)*. + + +With Gym: + +1๏ธโƒฃ We create our environment using `gym.make()` + +2๏ธโƒฃ We reset the environment to its initial state with `observation = env.reset()` + +At each step: + +3๏ธโƒฃ Get an action using our model (in our example we take a random action) + +4๏ธโƒฃ Using `env.step(action)`, we perform this action in the environment and get +- `observation`: The new state (st+1) +- `reward`: The reward we get after executing the action +- `done`: Indicates if the episode terminated +- `info`: A dictionary that provides additional information (depends on the environment). + +If the episode is done: +- We reset the environment to its initial state with `observation = env.reset()` + +**Let's look at an example!** Make sure to read the code + + +```python +import gym + +# First, we create our environment called LunarLander-v2 +env = gym.make("LunarLander-v2") + +# Then we reset this environment +observation = env.reset() + +for _ in range(20): + # Take a random action + action = env.action_space.sample() + print("Action taken:", action) + + # Do this action in the environment and get + # next_state, reward, done and info + observation, reward, done, info = env.step(action) + + # If the game is done (in our case we land, crashed or timeout) + if done: + # Reset the environment + print("Environment is reset") + observation = env.reset() +``` + +## Create the LunarLander environment ๐ŸŒ› and understand how it works +### [The environment ๐ŸŽฎ](https://www.gymlibrary.dev/environments/box2d/lunar_lander/) +In this first tutorial, weโ€™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.** + + +--- + + +๐Ÿ’ก A good habit when you start to use an environment is to check its documentation + +๐Ÿ‘‰ https://www.gymlibrary.dev/environments/box2d/lunar_lander/ + +--- + + +Let's see what the Environment looks like: + + +```python +# We create our environment with gym.make("") +env = gym.make("LunarLander-v2") +env.reset() +print("_____OBSERVATION SPACE_____ \n") +print("Observation Space Shape", env.observation_space.shape) +print("Sample observation", env.observation_space.sample()) # Get a random observation +``` + +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: +- Horizontal pad coordinate (x) +- Vertical pad coordinate (y) +- Horizontal speed (x) +- Vertical speed (y) +- Angle +- Angular speed +- If the left leg has contact point touched the land +- If the right leg has contact point touched the land + + +```python +print("\n _____ACTION SPACE_____ \n") +print("Action Space Shape", env.action_space.n) +print("Action Space Sample", env.action_space.sample()) # Take a random action +``` + +The action space (the set of possible actions the agent can take) is discrete with 4 actions available ๐ŸŽฎ: + +- Do nothing, +- Fire left orientation engine, +- Fire the main engine, +- Fire right orientation engine. + +Reward function (the function that will gives a reward at each timestep) ๐Ÿ’ฐ: + +- Moving from the top of the screen to the landing pad and zero speed is about 100~140 points. +- Firing main engine is -0.3 each frame +- Each leg ground contact is +10 points +- Episode finishes if the lander crashes (additional - 100 points) or come to rest (+100 points) + +#### Vectorized Environment +- 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.** + +```python +# Create the environment +env = make_vec_env("LunarLander-v2", n_envs=16) +``` + +## Create the Model ๐Ÿค– +- 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 ๐Ÿš€. + +- To do so, we're going to use our first Deep RL library, [Stable Baselines3 (SB3)](https://stable-baselines3.readthedocs.io/en/master/). + +- SB3 is a set of **reliable implementations of reinforcement learning algorithms in PyTorch**. + +--- + +๐Ÿ’ก 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. + +---- + +![image.png](data:image/png;base64,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) + +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). + +PPO is a combination of: +- *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**. +- *Policy-based reinforcement learning method*: learning a policy that will **gives us a probability distribution over actions**. + + +Stable-Baselines3 is easy to set up: + +1๏ธโƒฃ You **create your environment** (in our case it was done above) + +2๏ธโƒฃ You define the **model you want to use and instantiate this model** `model = PPO("MlpPolicy")` + +3๏ธโƒฃ You **train the agent** with `model.learn` and define the number of training timesteps + +``` +# Create environment +env = gym.make('LunarLander-v2') + +# Instantiate the agent +model = PPO('MlpPolicy', env, verbose=1) +# Train the agent +model.learn(total_timesteps=int(2e5)) +``` + + + +```python +# TODO: Define a PPO MlpPolicy architecture +# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector, +# if we had frames as input we would use CnnPolicy +model = +``` + +#### Solution + +```python +# SOLUTION +# We added some parameters to accelerate the training +model = PPO( + policy="MlpPolicy", + env=env, + n_steps=1024, + batch_size=64, + n_epochs=4, + gamma=0.999, + gae_lambda=0.98, + ent_coef=0.01, + verbose=1, +) +``` + +## Train the PPO agent ๐Ÿƒ +- 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. +- During the training, take a โ˜• break you deserved it ๐Ÿค— + +```python +# TODO: Train it for 1,000,000 timesteps + +# TODO: Specify file name for model and save the model to file +model_name = "" +``` + +#### Solution + +```python +# SOLUTION +# Train it for 1,000,000 timesteps +model.learn(total_timesteps=1000000) +# Save the model +model_name = "ppo-LunarLander-v2" +model.save(model_name) +``` + +## Evaluate the agent ๐Ÿ“ˆ +- Now that our Lunar Lander agent is trained ๐Ÿš€, we need to **check its performance**. +- Stable-Baselines3 provides a method to do that: `evaluate_policy`. +- 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) +- 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** + + +๐Ÿ’ก When you evaluate your agent, you should not use your training environment but create an evaluation environment. + +```python +# TODO: Evaluate the agent +# Create a new environment for evaluation +eval_env = + +# Evaluate the model with 10 evaluation episodes and deterministic=True +mean_reward, std_reward = + +# Print the results +``` + +#### Solution + +```python +# @title +eval_env = gym.make("LunarLander-v2") +mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) +print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") +``` + +- 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 ๐ŸŒ›๐Ÿฅณ. + +## Publish our trained model on the Hub ๐Ÿ”ฅ +Now that we saw we got good results after the training, we can publish our trained model on the hub ๐Ÿค— with one line of code. + +๐Ÿ“š The libraries documentation ๐Ÿ‘‰ https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face--x-stable-baselines3-v20 + +Here's an example of a Model Card (with Space Invaders): + +By using `package_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the hub**. + +This way: +- You can **showcase our work** ๐Ÿ”ฅ +- You can **visualize your agent playing** ๐Ÿ‘€ +- You can **share with the community an agent that others can use** ๐Ÿ’พ +- You can **access a leaderboard ๐Ÿ† to see how well your agent is performing compared to your classmates** ๐Ÿ‘‰ https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard + + +To be able to share your model with the community there are three more steps to follow: + +1๏ธโƒฃ (If it's not already done) create an account to HF โžก https://huggingface.co/join + +2๏ธโƒฃ Sign in and then, you need to store your authentication token from the Hugging Face website. +- Create a new token (https://huggingface.co/settings/tokens) **with write role** + +Create HF Token + +- Copy the token +- Run the cell below and paste the token + +```python +notebook_login() +!git config --global credential.helper store +``` + +If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login` + +3๏ธโƒฃ We're now ready to push our trained agent to the ๐Ÿค— Hub ๐Ÿ”ฅ using `package_to_hub()` function + +Let's fill the `package_to_hub` function: +- `model`: our trained model. +- `model_name`: the name of the trained model that we defined in `model_save` +- `model_architecture`: the model architecture we used: in our case PPO +- `env_id`: the name of the environment, in our case `LunarLander-v2` +- `eval_env`: the evaluation environment defined in eval_env +- `repo_id`: the name of the Hugging Face Hub Repository that will be created/updated `(repo_id = {username}/{repo_name})` + +๐Ÿ’ก **A good name is {username}/{model_architecture}-{env_id}** + +- `commit_message`: message of the commit + +```python +import gym +from stable_baselines3.common.vec_env import DummyVecEnv +from stable_baselines3.common.env_util import make_vec_env + +from huggingface_sb3 import package_to_hub + +## TODO: Define a repo_id +## 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 +repo_id = + +# TODO: Define the name of the environment +env_id = + +# Create the evaluation env +eval_env = DummyVecEnv([lambda: gym.make(env_id)]) + + +# TODO: Define the model architecture we used +model_architecture = "" + +## TODO: Define the commit message +commit_message = "" + +# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub +package_to_hub(model=model, # Our trained model + model_name=model_name, # The name of our trained model + model_architecture=model_architecture, # The model architecture we used: in our case PPO + env_id=env_id, # Name of the environment + eval_env=eval_env, # Evaluation Environment + 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 + commit_message=commit_message) + +# Note: if after running the package_to_hub function and it gives an issue of rebasing, please run the following code +# cd && git add . && git commit -m "Add message" && git pull +# And don't forget to do a "git push" at the end to push the change to the hub. +``` + +#### Solution + + +```python +import gym + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import DummyVecEnv +from stable_baselines3.common.env_util import make_vec_env + +from huggingface_sb3 import package_to_hub + +# PLACE the variables you've just defined two cells above +# Define the name of the environment +env_id = "LunarLander-v2" + +# TODO: Define the model architecture we used +model_architecture = "PPO" + +## Define a repo_id +## 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 +## CHANGE WITH YOUR REPO ID +repo_id = "ThomasSimonini/ppo-LunarLander-v2" # Change with your repo id, you can't push with mine ๐Ÿ˜„ + +## Define the commit message +commit_message = "Upload PPO LunarLander-v2 trained agent" + +# Create the evaluation env +eval_env = DummyVecEnv([lambda: gym.make(env_id)]) + +# PLACE the package_to_hub function you've just filled here +package_to_hub( + model=model, # Our trained model + model_name=model_name, # The name of our trained model + model_architecture=model_architecture, # The model architecture we used: in our case PPO + env_id=env_id, # Name of the environment + eval_env=eval_env, # Evaluation Environment + 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 + commit_message=commit_message, +) +``` + +Congrats ๐Ÿฅณ you've just trained and uploaded your first Deep Reinforcement Learning agent. The script above should have displayed a link to a model repository such as https://huggingface.co/osanseviero/test_sb3. When you go to this link, you can: +* see a video preview of your agent at the right. +* click "Files and versions" to see all the files in the repository. +* click "Use in stable-baselines3" to get a code snippet that shows how to load the model. +* a model card (`README.md` file) which gives a description of the model + +Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent. + +Compare the results of your LunarLander-v2 with your classmates using the leaderboard ๐Ÿ† ๐Ÿ‘‰ https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard + +## Load a saved LunarLander model from the Hub ๐Ÿค— +Thanks to [ironbar](https://github.com/ironbar) for the contribution. + +Loading a saved model from the Hub is really easy. + +You go https://huggingface.co/models?library=stable-baselines3 to see the list of all the Stable-baselines3 saved models. +1. You select one and copy its repo_id + +Copy-id + +2. Then we just need to use load_from_hub with: +- The repo_id +- The filename: the saved model inside the repo and its extension (*.zip) + +```python +from huggingface_sb3 import load_from_hub + +repo_id = "Classroom-workshop/assignment2-omar" # The repo_id +filename = "ppo-LunarLander-v2.zip" # The model filename.zip + +# When the model was trained on Python 3.8 the pickle protocol is 5 +# But Python 3.6, 3.7 use protocol 4 +# In order to get compatibility we need to: +# 1. Install pickle5 (we done it at the beginning of the colab) +# 2. Create a custom empty object we pass as parameter to PPO.load() +custom_objects = { + "learning_rate": 0.0, + "lr_schedule": lambda _: 0.0, + "clip_range": lambda _: 0.0, +} + +checkpoint = load_from_hub(repo_id, filename) +model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) +``` + +Let's evaluate this agent: + +```python +# @title +eval_env = gym.make("LunarLander-v2") +mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) +print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") +``` + +## Some additional challenges ๐Ÿ† +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! + +In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top? + +Here are some ideas to achieve so: +* Train more steps +* Try different hyperparameters of `PPO`. You can see them at https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#parameters. +* Check the [Stable-Baselines3 documentation](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) and try another models such as DQN. +* **Push your new trained model** on the Hub ๐Ÿ”ฅ + +**Compare the results of your LunarLander-v2 with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) ๐Ÿ† + +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 ๐ŸŽ‰. + +________________________________________________________________________ +Congrats on finishing this chapter! That was the biggest one, **and there was a lot of information.** + +If youโ€™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.** + +Take time to really **grasp the material before continuing and try the additional challenges**. Itโ€™s important to master these elements and having a solid foundations. + +Naturally, during the course, weโ€™re going to use and deeper explain again these terms but **itโ€™s better to have a good understanding of them now before diving into the next chapters.** + + +Next time, in the bonus unit 1, you'll train Huggy the Dog to fetch the stick. + +Huggy + +## Keep learning, stay awesome ๐Ÿค— \ No newline at end of file diff --git a/units/en/unit1/hands-on.mdx b/units/en/unit1/hands-on.mdx index b777055..09dec7a 100644 --- a/units/en/unit1/hands-on.mdx +++ b/units/en/unit1/hands-on.mdx @@ -15,3 +15,634 @@ So let's get started! ๐Ÿš€ **To start the hands-on click on Open In Colab button** ๐Ÿ‘‡ : [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]() + + +# Unit 1: Train your first Deep Reinforcement Learning Agent ๐Ÿค– +![Cover](https://github.com/huggingface/deep-rl-class/blob/main/unit1/assets/img/thumbnail.png?raw=true) + +In this notebook, you'll train your **first Deep Reinforcement Learning agent** a Lunar Lander agent that will learn to **land correctly on the Moon ๐ŸŒ•**. Using [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) a Deep Reinforcement Learning library, share them with the community, and experiment with different configurations + +โฌ‡๏ธ Here is an example of what **you will achieve in just a couple of minutes.** โฌ‡๏ธ + + + + +```python +%%html + +``` + +### The environment ๐ŸŽฎ + +- [LunarLander-v2](https://www.gymlibrary.dev/environments/box2d/lunar_lander/) + +### The library used ๐Ÿ“š + +- [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) + +We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues). + +## Objectives of this notebook ๐Ÿ† + +At the end of the notebook, you will: + +- Be able to use **Gym**, the environment library. +- Be able to use **Stable-Baselines3**, the deep reinforcement learning library. +- Be able to **push your trained agent to the Hub** with a nice video replay and an evaluation score ๐Ÿ”ฅ. + + + + +## This notebook is from Deep Reinforcement Learning Course +Deep RL Course illustration + +In this free course, you will: + +- ๐Ÿ“– Study Deep Reinforcement Learning in **theory and practice**. +- ๐Ÿง‘โ€๐Ÿ’ป Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0. +- ๐Ÿค– Train **agents in unique environments** + +And more check ๐Ÿ“š the syllabus ๐Ÿ‘‰ https://simoninithomas.github.io/deep-rl-course + +Donโ€™t forget to **sign up to the course** (we are collecting your email to be able toย **send you the links when each Unit is published and give you information about the challenges and updates).** + + +The best way to keep in touch and ask questions is to join our discord server to exchange with the community and with us ๐Ÿ‘‰๐Ÿป https://discord.gg/ydHrjt3WP5 + +## Prerequisites ๐Ÿ—๏ธ +Before diving into the notebook, you need to: + +๐Ÿ”ฒ ๐Ÿ“ **Done Unit 0** that gives you all the **information about the course and help you to onboard** ๐Ÿค— ADD LINK + +๐Ÿ”ฒ ๐Ÿ“š **Develop an understanding of the foundations of Reinforcement learning** (MC, TD, Rewards hypothesis...) by doing Unit 1 ๐Ÿ‘‰ ADD LINK + +## A small recap of what is Deep Reinforcement Learning ๐Ÿ“š +The RL process + +Let's do a small recap on what we learned in the first Unit: +- Reinforcement Learning is a **computational approach to learning from action**. We build an agent that learns from the environment by **interacting with it through trial and error** and receiving rewards (negative or positive) as feedback. + +- The goal of any RL agent is to **maximize its expected cumulative reward** (also called expected return) because RL is based on the _reward hypothesis_, which is that all goals can be described as the maximization of the expected cumulative reward. + +- The RL process is a **loop that outputs a sequence of state, action, reward, and next state**. + +- To calculate the expected cumulative reward (expected return), **we discount the rewards**: the rewards that come sooner (at the beginning of the game) are more probable to happen since they are more predictable than the long-term future reward. + +- To solve an RL problem, you want to **find an optimal policy**; the policy is the "brain" of your AI that will tell us what action to take given a state. The optimal one is the one that gives you the actions that max the expected return. + + +There are **two** ways to find your optimal policy: +- By **training your policy directly**: policy-based methods. +- By **training a value function** that tells us the expected return the agent will get at each state and use this function to define our policy: value-based methods. + +- Finally, we spoke about Deep RL because **we introduce deep neural networks to estimate the action to take (policy-based) or to estimate the value of a state (value-based) hence the name "deep."** + +# Let's train our first Deep Reinforcement Learning agent and upload it to the Hub ๐Ÿš€ + + +## Set the GPU ๐Ÿ’ช +- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type` + +GPU Step 1 + +- `Hardware Accelerator > GPU` + +GPU Step 2 + +## Install dependencies and create a virtual screen ๐Ÿ”ฝ +The first step is to install the dependencies, weโ€™ll install multiple ones. + +- `gym[box2D]`: Contains the LunarLander-v2 environment ๐ŸŒ› (we use `gym==0.21`) +- `stable-baselines3[extra]`: The deep reinforcement learning library. +- `huggingface_sb3`: Additional code for Stable-baselines3 to load and upload models from the Hugging Face ๐Ÿค— Hub. + +To make things easier, we created a script to install all these dependencies. + +```python +!apt install swig cmake +``` + +TODO CHANGE LINK OF THE REQUIREMENTS + +```python +!pip install -r https://huggingface.co/spaces/ThomasSimonini/temp-space-requirements/raw/main/requirements/requirements-unit1.txt +``` + +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). + +Hence the following cell will install virtual screen libraries and create and run a virtual screen ๐Ÿ–ฅ + +```python +!sudo apt-get update +!apt install python-opengl +!apt install ffmpeg +!apt install xvfb +!pip3 install pyvirtualdisplay +``` + +```python +# Virtual display +from pyvirtualdisplay import Display + +virtual_display = Display(visible=0, size=(1400, 900)) +virtual_display.start() +``` + +## Import the packages ๐Ÿ“ฆ + +One additional library we import is huggingface_hub **to be able to upload and download trained models from the hub**. + + +The Hugging Face Hub ๐Ÿค— 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. + +You can see here all the Deep reinforcement Learning models available ๐Ÿ‘‰ https://huggingface.co/models?pipeline_tag=reinforcement-learning&sort=downloads + + + +```python +import gym + +from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub +from huggingface_hub import ( + notebook_login, +) # To log to our Hugging Face account to be able to upload models to the Hub. + +from stable_baselines3 import PPO +from stable_baselines3.common.evaluation import evaluate_policy +from stable_baselines3.common.env_util import make_vec_env +``` + +## Understand what is Gym and how it works ๐Ÿค– + +๐Ÿ‹ The library containing our environment is called Gym. +**You'll use Gym a lot in Deep Reinforcement Learning.** + +The Gym library provides two things: +- An interface that allows you to **create RL environments**. +- A **collection of environments** (gym-control, atari, box2D...). + +Let's look at an example, but first let's remember what's the RL Loop. + +The RL process + +At each step: +- Our Agent receivesย **state S0**ย from theย **Environment**ย โ€” we receive the first frame of our game (Environment). +- Based on thatย **state S0,**ย the Agent takesย **action A0**ย โ€” our Agent will move to the right. +- Environment to aย **new**ย **state S1**ย โ€” new frame. +- The environment gives someย **reward R1**ย to the Agent โ€” weโ€™re not deadย *(Positive Reward +1)*. + + +With Gym: + +1๏ธโƒฃ We create our environment using `gym.make()` + +2๏ธโƒฃ We reset the environment to its initial state with `observation = env.reset()` + +At each step: + +3๏ธโƒฃ Get an action using our model (in our example we take a random action) + +4๏ธโƒฃ Using `env.step(action)`, we perform this action in the environment and get +- `observation`: The new state (st+1) +- `reward`: The reward we get after executing the action +- `done`: Indicates if the episode terminated +- `info`: A dictionary that provides additional information (depends on the environment). + +If the episode is done: +- We reset the environment to its initial state with `observation = env.reset()` + +**Let's look at an example!** Make sure to read the code + + +```python +import gym + +# First, we create our environment called LunarLander-v2 +env = gym.make("LunarLander-v2") + +# Then we reset this environment +observation = env.reset() + +for _ in range(20): + # Take a random action + action = env.action_space.sample() + print("Action taken:", action) + + # Do this action in the environment and get + # next_state, reward, done and info + observation, reward, done, info = env.step(action) + + # If the game is done (in our case we land, crashed or timeout) + if done: + # Reset the environment + print("Environment is reset") + observation = env.reset() +``` + +## Create the LunarLander environment ๐ŸŒ› and understand how it works + +### The environment ๐ŸŽฎ + +In this first tutorial, weโ€™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.** + + +--- + + +๐Ÿ’ก A good habit when you start to use an environment is to check its documentation + +๐Ÿ‘‰ https://www.gymlibrary.dev/environments/box2d/lunar_lander/ + +--- + + +Let's see what the Environment looks like: + + +```python +# We create our environment with gym.make("") +env = gym.make("LunarLander-v2") +env.reset() +print("_____OBSERVATION SPACE_____ \n") +print("Observation Space Shape", env.observation_space.shape) +print("Sample observation", env.observation_space.sample()) # Get a random observation +``` + +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: +- Horizontal pad coordinate (x) +- Vertical pad coordinate (y) +- Horizontal speed (x) +- Vertical speed (y) +- Angle +- Angular speed +- If the left leg has contact point touched the land +- If the right leg has contact point touched the land + + +```python +print("\n _____ACTION SPACE_____ \n") +print("Action Space Shape", env.action_space.n) +print("Action Space Sample", env.action_space.sample()) # Take a random action +``` + +The action space (the set of possible actions the agent can take) is discrete with 4 actions available ๐ŸŽฎ: + +- Do nothing, +- Fire left orientation engine, +- Fire the main engine, +- Fire right orientation engine. + +Reward function (the function that will gives a reward at each timestep) ๐Ÿ’ฐ: + +- Moving from the top of the screen to the landing pad and zero speed is about 100~140 points. +- Firing main engine is -0.3 each frame +- Each leg ground contact is +10 points +- Episode finishes if the lander crashes (additional - 100 points) or come to rest (+100 points) + +#### Vectorized Environment + +- 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.** + +```python +# Create the environment +env = make_vec_env("LunarLander-v2", n_envs=16) +``` + +## Create the Model ๐Ÿค– + +- 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 ๐Ÿš€. + +- To do so, we're going to use our first Deep RL library, [Stable Baselines3 (SB3)](https://stable-baselines3.readthedocs.io/en/master/). + +- SB3 is a set of **reliable implementations of reinforcement learning algorithms in PyTorch**. + +--- + +๐Ÿ’ก 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. + +---- + +![image.png](data:image/png;base64,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) + +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). + +PPO is a combination of: + +- *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**. +- *Policy-based reinforcement learning method*: learning a policy that will **gives us a probability distribution over actions**. + + +Stable-Baselines3 is easy to set up: + +1๏ธโƒฃ You **create your environment** (in our case it was done above) + +2๏ธโƒฃ You define the **model you want to use and instantiate this model** `model = PPO("MlpPolicy")` + +3๏ธโƒฃ You **train the agent** with `model.learn` and define the number of training timesteps + +``` +# Create environment + +env = gym.make('LunarLander-v2') + +# Instantiate the agent +model = PPO('MlpPolicy', env, verbose=1) +# Train the agent +model.learn(total_timesteps=int(2e5)) +``` + + + +```python +# TODO: Define a PPO MlpPolicy architecture +# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector, +# if we had frames as input we would use CnnPolicy +model = +``` + +#### Solution + +```python +# SOLUTION +# We added some parameters to accelerate the training +model = PPO( + policy="MlpPolicy", + env=env, + n_steps=1024, + batch_size=64, + n_epochs=4, + gamma=0.999, + gae_lambda=0.98, + ent_coef=0.01, + verbose=1, +) +``` + +## Train the PPO agent ๐Ÿƒ + +- 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. +- During the training, take a โ˜• break you deserved it ๐Ÿค— + +```python +# TODO: Train it for 1,000,000 timesteps + +# TODO: Specify file name for model and save the model to file +model_name = "" +``` + +#### Solution + +```python +# SOLUTION +# Train it for 1,000,000 timesteps +model.learn(total_timesteps=1000000) +# Save the model +model_name = "ppo-LunarLander-v2" +model.save(model_name) +``` + +## Evaluate the agent ๐Ÿ“ˆ + +- Now that our Lunar Lander agent is trained ๐Ÿš€, we need to **check its performance**. +- Stable-Baselines3 provides a method to do that: `evaluate_policy`. +- 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) +- 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** + + +๐Ÿ’ก When you evaluate your agent, you should not use your training environment but create an evaluation environment. + +```python +# TODO: Evaluate the agent +# Create a new environment for evaluation +eval_env = + +# Evaluate the model with 10 evaluation episodes and deterministic=True +mean_reward, std_reward = + +# Print the results +``` + +#### Solution + +```python +# @title +eval_env = gym.make("LunarLander-v2") +mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) +print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") +``` + +- 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 ๐ŸŒ›๐Ÿฅณ. + +## Publish our trained model on the Hub ๐Ÿ”ฅ +Now that we saw we got good results after the training, we can publish our trained model on the hub ๐Ÿค— with one line of code. + +๐Ÿ“š The libraries documentation ๐Ÿ‘‰ https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face--x-stable-baselines3-v20 + +Here's an example of a Model Card (with Space Invaders): + +By using `package_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the hub**. + +This way: +- You can **showcase our work** ๐Ÿ”ฅ +- You can **visualize your agent playing** ๐Ÿ‘€ +- You can **share with the community an agent that others can use** ๐Ÿ’พ +- You can **access a leaderboard ๐Ÿ† to see how well your agent is performing compared to your classmates** ๐Ÿ‘‰ https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard + + +To be able to share your model with the community there are three more steps to follow: + +1๏ธโƒฃ (If it's not already done) create an account to HF โžก https://huggingface.co/join + +2๏ธโƒฃ Sign in and then, you need to store your authentication token from the Hugging Face website. +- Create a new token (https://huggingface.co/settings/tokens) **with write role** + +Create HF Token + +- Copy the token +- Run the cell below and paste the token + +```python +notebook_login() +!git config --global credential.helper store +``` + +If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login` + +3๏ธโƒฃ We're now ready to push our trained agent to the ๐Ÿค— Hub ๐Ÿ”ฅ using `package_to_hub()` function + +Let's fill the `package_to_hub` function: +- `model`: our trained model. +- `model_name`: the name of the trained model that we defined in `model_save` +- `model_architecture`: the model architecture we used: in our case PPO +- `env_id`: the name of the environment, in our case `LunarLander-v2` +- `eval_env`: the evaluation environment defined in eval_env +- `repo_id`: the name of the Hugging Face Hub Repository that will be created/updated `(repo_id = {username}/{repo_name})` + +๐Ÿ’ก **A good name is {username}/{model_architecture}-{env_id}** + +- `commit_message`: message of the commit + +```python +import gym +from stable_baselines3.common.vec_env import DummyVecEnv +from stable_baselines3.common.env_util import make_vec_env + +from huggingface_sb3 import package_to_hub + +## TODO: Define a repo_id +## 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 +repo_id = + +# TODO: Define the name of the environment +env_id = + +# Create the evaluation env +eval_env = DummyVecEnv([lambda: gym.make(env_id)]) + + +# TODO: Define the model architecture we used +model_architecture = "" + +## TODO: Define the commit message +commit_message = "" + +# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub +package_to_hub(model=model, # Our trained model + model_name=model_name, # The name of our trained model + model_architecture=model_architecture, # The model architecture we used: in our case PPO + env_id=env_id, # Name of the environment + eval_env=eval_env, # Evaluation Environment + 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 + commit_message=commit_message) + +# Note: if after running the package_to_hub function and it gives an issue of rebasing, please run the following code +# cd && git add . && git commit -m "Add message" && git pull +# And don't forget to do a "git push" at the end to push the change to the hub. +``` + +#### Solution + + +```python +import gym + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import DummyVecEnv +from stable_baselines3.common.env_util import make_vec_env + +from huggingface_sb3 import package_to_hub + +# PLACE the variables you've just defined two cells above +# Define the name of the environment +env_id = "LunarLander-v2" + +# TODO: Define the model architecture we used +model_architecture = "PPO" + +## Define a repo_id +## 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 +## CHANGE WITH YOUR REPO ID +repo_id = "ThomasSimonini/ppo-LunarLander-v2" # Change with your repo id, you can't push with mine ๐Ÿ˜„ + +## Define the commit message +commit_message = "Upload PPO LunarLander-v2 trained agent" + +# Create the evaluation env +eval_env = DummyVecEnv([lambda: gym.make(env_id)]) + +# PLACE the package_to_hub function you've just filled here +package_to_hub( + model=model, # Our trained model + model_name=model_name, # The name of our trained model + model_architecture=model_architecture, # The model architecture we used: in our case PPO + env_id=env_id, # Name of the environment + eval_env=eval_env, # Evaluation Environment + 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 + commit_message=commit_message, +) +``` + +Congrats ๐Ÿฅณ you've just trained and uploaded your first Deep Reinforcement Learning agent. The script above should have displayed a link to a model repository such as https://huggingface.co/osanseviero/test_sb3. When you go to this link, you can: +* see a video preview of your agent at the right. +* click "Files and versions" to see all the files in the repository. +* click "Use in stable-baselines3" to get a code snippet that shows how to load the model. +* a model card (`README.md` file) which gives a description of the model + +Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent. + +Compare the results of your LunarLander-v2 with your classmates using the leaderboard ๐Ÿ† ๐Ÿ‘‰ https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard + +## Load a saved LunarLander model from the Hub ๐Ÿค— +Thanks to [ironbar](https://github.com/ironbar) for the contribution. + +Loading a saved model from the Hub is really easy. + +You go https://huggingface.co/models?library=stable-baselines3 to see the list of all the Stable-baselines3 saved models. +1. You select one and copy its repo_id + +Copy-id + +2. Then we just need to use load_from_hub with: +- The repo_id +- The filename: the saved model inside the repo and its extension (*.zip) + +```python +from huggingface_sb3 import load_from_hub + +repo_id = "Classroom-workshop/assignment2-omar" # The repo_id +filename = "ppo-LunarLander-v2.zip" # The model filename.zip + +# When the model was trained on Python 3.8 the pickle protocol is 5 +# But Python 3.6, 3.7 use protocol 4 +# In order to get compatibility we need to: +# 1. Install pickle5 (we done it at the beginning of the colab) +# 2. Create a custom empty object we pass as parameter to PPO.load() +custom_objects = { + "learning_rate": 0.0, + "lr_schedule": lambda _: 0.0, + "clip_range": lambda _: 0.0, +} + +checkpoint = load_from_hub(repo_id, filename) +model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) +``` + +Let's evaluate this agent: + +```python +# @title +eval_env = gym.make("LunarLander-v2") +mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) +print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") +``` + +## Some additional challenges ๐Ÿ† +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! + +In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top? + +Here are some ideas to achieve so: +* Train more steps +* Try different hyperparameters of `PPO`. You can see them at https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#parameters. +* Check the [Stable-Baselines3 documentation](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) and try another models such as DQN. +* **Push your new trained model** on the Hub ๐Ÿ”ฅ + +**Compare the results of your LunarLander-v2 with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) ๐Ÿ† + +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 ๐ŸŽ‰. + +________________________________________________________________________ +Congrats on finishing this chapter! That was the biggest one, **and there was a lot of information.** + +If youโ€™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.** + +Take time to really **grasp the material before continuing and try the additional challenges**. Itโ€™s important to master these elements and having a solid foundations. + +Naturally, during the course, weโ€™re going to use and deeper explain again these terms but **itโ€™s better to have a good understanding of them now before diving into the next chapters.** + + +Next time, in the bonus unit 1, you'll train Huggy the Dog to fetch the stick. + +Huggy + +## Keep learning, stay awesome ๐Ÿค—