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README.md
View file @ fd4d64f2
# Hands-On Reinforcement Learning # TD 1 : Hands-On Reinforcement Learning
MSO 3.4 Apprentissage Automatique
#
In this hands-on project, we will first implement a simple RL algorithm and apply it to solve the CartPole-v1 environment. Once we become familiar with the basic workflow, we will learn to use various tools for machine learning model training, monitoring, and sharing, by applying these tools to train a robotic arm. In this hands-on project, we will first implement a simple RL algorithm and apply it to solve the CartPole-v1 environment. Once we become familiar with the basic workflow, we will learn to use various tools for machine learning model training, monitoring, and sharing, by applying these tools to train a robotic arm.
## To be handed in ## To be handed in
This work must be done individually. The expected output is a repository named `hands-on-rl` on https://gitlab.ec-lyon.fr. It must contain a `README.md` file that explains **briefly** the successive steps of the project. Throughout the subject, you will find a 🛠 symbol indicating that a specific production is expected. This work must be done individually. The expected output is a repository named `hands-on-rl` on https://gitlab.ec-lyon.fr.
The last commit is due before 11:59 pm on Monday, February 13, 2023. Subsequent commits will not be considered.
We assume that `git` is installed, and that you are familiar with the basic `git` commands. (Optionnaly, you can use GitHub Desktop.)
We also assume that you have access to the [ECL GitLab](https://gitlab.ec-lyon.fr/). If necessary, please consult [this tutorial](https://gitlab.ec-lyon.fr/edelland/inf_tc2/-/blob/main/Tutoriel_gitlab/tutoriel_gitlab.md).
Your repository must contain a `README.md` file that explains **briefly** the successive steps of the project. It must be private, so you need to add your teacher as "developer" member.
Throughout the subject, you will find a 🛠 symbol indicating that a specific production is expected.
The last commit is due before 11:59 pm on March 17, 2025. Subsequent commits will not be considered.
> ⚠️ **Warning** > ⚠️ **Warning**
> Ensure that you only commit the files that are requested. For example, your directory should not contain the generated `.zip` files, nor the `runs` folder... At the end, your repository must contain one `README.md`, three python scripts, and optionally image files for the plots. > Ensure that you only commit the files that are requested. For example, your directory should not contain the generated `.zip` files, nor the `runs` folder... At the end, your repository must contain one `README.md`, three python scripts, and optionally image files for the plots.
...@@ -16,29 +28,35 @@ Make sure you know the basics of Reinforcement Learning. In case of need, you ca ...@@ -16,29 +28,35 @@ Make sure you know the basics of Reinforcement Learning. In case of need, you ca
## Introduction to Gym ## Introduction to Gym
Gym is a framework for developing and evaluating reinforcement learning environments. It offers various environments, including classic control and toy text scenarios, to test RL algorithms. [Gym](https://gymnasium.farama.org/) is a framework for developing and evaluating reinforcement learning environments. It offers various environments, including classic control and toy text scenarios, to test RL algorithms.
### Installation ### Installation
We recommend to use Python virtual environnements to install the required modules : https://docs.python.org/3/library/venv.html or https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html if you are using conda.
First, install Pytorch : https://pytorch.org/get-started/locally.
Then install the following modules :
```sh ```sh
pip install gym==0.21 pip install gymnasium
``` ```
Install also pyglet for the rendering.
```sh ```sh
pip install pyglet==1.5.27 pip install "gymnasium[classic-control]"
``` ```
### Usage ### Usage
Here is an example of how to use Gym to solve the `CartPole-v1` environment: Here is an example of how to use Gym to solve the `CartPole-v1` environment [Documentation](https://gymnasium.farama.org/environments/classic_control/cart_pole/):
```python ```python
import gym import gymnasium as gym
# Create the environment # Create the environment
env = gym.make("CartPole-v1") env = gym.make("CartPole-v1", render_mode="human")
# Reset the environment and get the initial observation # Reset the environment and get the initial observation
observation = env.reset() observation = env.reset()
...@@ -49,9 +67,14 @@ for _ in range(100): ...@@ -49,9 +67,14 @@ for _ in range(100):
# Apply the action to the environment # Apply the action to the environment
# Returns next observation, reward, done signal (indicating # Returns next observation, reward, done signal (indicating
# if the episode has ended), and an additional info dictionary # if the episode has ended), and an additional info dictionary
observation, reward, done, info = env.step(action) observation, reward, terminated, truncated, info = env.step(action)
# Render the environment to visualize the agent's behavior # Render the environment to visualize the agent's behavior
env.render() env.render()
if terminated:
# Terminated before max step
break
env.close()
``` ```
## REINFORCE ## REINFORCE
...@@ -74,12 +97,21 @@ Repeat 500 times: ...@@ -74,12 +97,21 @@ Repeat 500 times:
Normalize the return Normalize the return
Compute the policy loss as -sum(log(prob) * return) Compute the policy loss as -sum(log(prob) * return)
Update the policy using an Adam optimizer and a learning rate of 5e-3 Update the policy using an Adam optimizer and a learning rate of 5e-3
Save the model weights
``` ```
To learn more about REINFORCE, you can refer to [this unit](https://huggingface.co/blog/deep-rl-pg). To learn more about REINFORCE, you can refer to [this unit](https://huggingface.co/learn/deep-rl-course/unit4/policy-gradient).
> 🛠 **To be handed in**
> Use PyTorch to implement REINFORCE and solve the CartPole environement. Share the code in `reinforce_cartpole.py`, and share a plot showing the total reward accross episodes in the `README.md`. Also, share a file `reinforce_cartpole.pth` containing the learned weights. For saving and loading PyTorch models, check [this tutorial](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference)
## Model Evaluation
Now that you have trained your model, it is time to evaluate its performance. Run it with rendering for a few trials and see if the policy is capable of completing the task.
> 🛠 **To be handed in** > 🛠 **To be handed in**
> Use PyTorch to implement REINFORCE and solve the CartPole environement. Share the code in `reinforce_cartpole.py`, and share a plot showing the total reward accross episodes in the `README.md`. > Implement a script which loads your saved model and use it to solve the cartpole enviroment. Run 100 evaluations and share the final success rate across all evaluations in the `README.md`. Share the code in `evaluate_reinforce_cartpole.py`.
## Familiarization with a complete RL pipeline: Application to training a robotic arm ## Familiarization with a complete RL pipeline: Application to training a robotic arm
...@@ -92,12 +124,11 @@ Stable-Baselines3 (SB3) is a high-level RL library that provides various algorit ...@@ -92,12 +124,11 @@ Stable-Baselines3 (SB3) is a high-level RL library that provides various algorit
#### Installation #### Installation
```sh ```sh
pip install stable-baselines3[extra] pip install stable-baselines3
pip install "stable-baselines3[extra]"
pip install moviepy
``` ```
> ⚠️ **Warning**
> If you use zsh as a shell, you'll need to use extra quote: `stable-baselines3"[extra]"`
#### Usage #### Usage
Use the [Stable-Baselines3 documentation](https://stable-baselines3.readthedocs.io/en/master/) to implement the code to solve the CartPole environment with the Advantage Actor-Critic (A2C) algorithm. Use the [Stable-Baselines3 documentation](https://stable-baselines3.readthedocs.io/en/master/) to implement the code to solve the CartPole environment with the Advantage Actor-Critic (A2C) algorithm.
...@@ -113,12 +144,12 @@ Hugging Face Hub is a platform for easy sharing and versioning of trained machin ...@@ -113,12 +144,12 @@ Hugging Face Hub is a platform for easy sharing and versioning of trained machin
#### Installation of `huggingface_sb3` #### Installation of `huggingface_sb3`
```sh ```sh
pip install huggingface_sb3 pip install huggingface-sb3
``` ```
#### Upload the model on the Hub #### Upload the model on the Hub
Follow the [Hugging Face Hub documentation](https://huggingface.co/docs/hub/index) to upload the previously learned model to the Hub. Follow the [Hugging Face Hub documentation](https://huggingface.co/docs/hub/stable-baselines3) to upload the previously learned model to the Hub.
> 🛠 **To be handed in** > 🛠 **To be handed in**
> Link the trained model in the `README.md` file. > Link the trained model in the `README.md` file.
...@@ -132,30 +163,32 @@ Weights & Biases (W&B) is a tool for machine learning experiment management. Wit ...@@ -132,30 +163,32 @@ Weights & Biases (W&B) is a tool for machine learning experiment management. Wit
#### Installation #### Installation
You'll need to install `wand`.
```shell ```shell
pip install wandb pip install wandb
``` ```
Use the documentation of Stable-Baselines3 and [Weights & Biases](https://docs.wandb.ai) to track the CartPole training. Make the run public. Use the documentation of [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) and [Weights & Biases](https://docs.wandb.ai/guides/integrations/stable-baselines-3) to track the CartPole training. Make the run public.
🛠 Share the link of the wandb run in the `README.md` file. 🛠 Share the link of the wandb run in the `README.md` file.
> ⚠️ **Warning** > ⚠️ **Warning**
> Make sure to make the run public! > Make sure to make the run public! If it is not possible (due to the restrictions on your account), you can create a WandB [report](https://docs.wandb.ai/guides/reports/create-a-report/), add all relevant graphs and any textual descriptions or explanations you find pertinent, then download a PDF file (landscape format) and upload it along with the code to GitLab. Make sure to arrange the plots in a way that makes them understandable in the PDF (e.g., one graph per row, correct axes, etc.). Specify which report corresponds to which experiment.
### Full workflow with panda-gym ### Full workflow with panda-gym
Panda-gym is a collection of environments for robotic simulation and control. It provides a range of challenges for training robotic agents in a simulated environment. In this section, you will get familiar with one of the environments provided by panda-gym, the `PandaReachJointsDense-v2`. The objective is to learn how to reach any point in 3D space by directly controlling the robot's articulations. [Panda-gym](https://github.com/qgallouedec/panda-gym) is a collection of environments for robotic simulation and control. It provides a range of challenges for training robotic agents in a simulated environment. In this section, you will get familiar with one of the environments provided by panda-gym, the `PandaReachJointsDense-v3`. The objective is to learn how to reach any point in 3D space by directly controlling the robot's articulations.
#### Installation #### Installation
```shell ```shell
pip install panda_gym==2.0.0 pip install panda-gym==3.0.7
``` ```
#### Train, track, and share #### Train, track, and share
Use the Stable-Baselines3 package to train A2C model on the `PandaReachJointsDense-v2` environment. 500k timesteps should be enough. Track the environment with Weights & Biases. Once the training is over, upload the trained model on the Hub. Use the Stable-Baselines3 package to train A2C model on the `PandaReachJointsDense-v3` environment. 500k timesteps should be enough. Track the environment with Weights & Biases. Once the training is over, upload the trained model on the Hub.
> 🛠 **To be handed in** > 🛠 **To be handed in**
> Share all the code in `a2c_sb3_panda_reach.py`. Share the link of the wandb run and the trained model in the `README.md` file. > Share all the code in `a2c_sb3_panda_reach.py`. Share the link of the wandb run and the trained model in the `README.md` file.
...@@ -168,6 +201,8 @@ This tutorial may contain errors, inaccuracies, typos or areas for improvement. ...@@ -168,6 +201,8 @@ This tutorial may contain errors, inaccuracies, typos or areas for improvement.
Quentin Gallouédec Quentin Gallouédec
Updates by Bruno Machado, Léo Schneider, Emmanuel Dellandréa
## License ## License
MIT MIT