diff --git a/README.md b/README.md
index c0693a2e274c7710b9d22073bff9f84d2f4cb349..4b5a201e6cfa8e4684abf6964b3a39c898a1c685 100644
--- a/README.md
+++ b/README.md
@@ -1,199 +1,47 @@
-# TD 1 : Hands-On Reinforcement Learning
+# Hands-On Reinforcement Learning
-MSO 3.4 Apprentissage Automatique
+## Introduction
-#
+Dans ce projet pratique, nous allons d'abord implémenter un algorithme simple de RL et l'appliquer pour résoudre l'environnement CartPole-v1. Une fois que nous aurons pris connaissance du flux de travail de base, nous apprendrons à utiliser divers outils pour l'entraînement, la surveillance et le partage de modèles d'apprentissage automatique, en appliquant ces outils pour entraîner un bras robotique.
-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.
+## Structure du Repository
-## To be handed in
+Ce repository contient les fichiers suivants :
-This work must be done individually. The expected output is a repository named `hands-on-rl` on https://gitlab.ec-lyon.fr.
+- `reinforce_cartpole.py`: Implémentation de l'algorithme REINFORCE pour résoudre l'environnement CartPole-v1.
+- `a2c_sb3_cartpole.py`: Utilisation de l'algorithme Advantage Actor-Critic (A2C) pour résoudre l'environnement CartPole-v1 avec Stable-Baselines3.
+- `a2c_sb3_panda_reach.py`: Entraînement d'un modèle A2C sur l'environnement PandaReachJointsDense-v2 avec Stable-Baselines3.
+- `README.md`: Ce fichier, fournissant des instructions sur le projet et les fichiers inclus.
-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).
+## Prérequis
-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.
+Avant de commencer, assurez-vous d'avoir installé les bibliothèques nécessaires. Vous pouvez les installer en utilisant `pip` :
-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 5, 2024. Subsequent commits will not be considered.
-
-> ⚠️ **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.
-
-## Before you start
-
-Make sure you know the basics of Reinforcement Learning. In case of need, you can refer to the [introduction of the Hugging Face RL course](https://huggingface.co/blog/deep-rl-intro).
-
-## Introduction to Gym
-
-[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
-
-We recommend to use Python virtual environnements to install the required modules : https://docs.python.org/3/library/venv.html
-
-First, install Pytorch : https://pytorch.org/get-started/locally.
-
-Then install the following modules :
-
-
-```sh
-pip install gym==0.26.2
-```
-
-Install also pyglet for the rendering.
-
-```sh
-pip install pyglet==2.0.10
-```
-
-If needed
-
-```sh
-pip install pygame==2.5.2
```
-
-```sh
-pip install PyQt5
-```
-
-
-### Usage
-
-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
-import gym
-
-# Create the environment
-env = gym.make("CartPole-v1", render_mode="human")
-
-# Reset the environment and get the initial observation
-observation = env.reset()
-
-for _ in range(100):
- # Select a random action from the action space
- action = env.action_space.sample()
- # Apply the action to the environment
- # Returns next observation, reward, done signal (indicating
- # if the episode has ended), and an additional info dictionary
- observation, reward, terminated, truncated, info = env.step(action)
- # Render the environment to visualize the agent's behavior
- env.render()
- if terminated:
- # Terminated before max step
- break
-
-env.close()
-```
-
-## REINFORCE
-
-The REINFORCE algorithm (also known as Vanilla Policy Gradient) is a policy gradient method that optimizes the policy directly using gradient descent. The following is the pseudocode of the REINFORCE algorithm:
-
-```txt
-Setup the CartPole environment
-Setup the agent as a simple neural network with:
- - One fully connected layer with 128 units and ReLU activation followed by a dropout layer
- - One fully connected layer followed by softmax activation
-Repeat 500 times:
- Reset the environment
- Reset the buffer
- Repeat until the end of the episode:
- Compute action probabilities
- Sample the action based on the probabilities and store its probability in the buffer
- Step the environment with the action
- Compute and store in the buffer the return using gamma=0.99
- Normalize the return
- Compute the policy loss as -sum(log(prob) * return)
- Update the policy using an Adam optimizer and a learning rate of 5e-3
-```
-
-To learn more about REINFORCE, you can refer to [this unit](https://huggingface.co/learn/deep-rl-course/unit4/introduction).
-
-> 🛠 **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`.
-
-## Familiarization with a complete RL pipeline: Application to training a robotic arm
-
-In this section, you will use the Stable-Baselines3 package to train a robotic arm using RL. You'll get familiar with several widely-used tools for training, monitoring and sharing machine learning models.
-
-### Get familiar with Stable-Baselines3
-
-Stable-Baselines3 (SB3) is a high-level RL library that provides various algorithms and integrated tools to easily train and test reinforcement learning models.
-
-#### Installation
-
-```sh
pip install stable-baselines3
-pip install moviepy
-```
-
-#### 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.
-
-
-> 🛠 **To be handed in**
-> Store the code in `a2c_sb3_cartpole.py`. Unless otherwise stated, you'll work upon this file for the next sections.
-
-### Get familiar with Hugging Face Hub
-
-Hugging Face Hub is a platform for easy sharing and versioning of trained machine learning models. With Hugging Face Hub, you can quickly and easily share your models with others and make them usable through the API. For example, see the trained A2C agent for CartPole: https://huggingface.co/sb3/a2c-CartPole-v1. Hugging Face Hub provides an API to download and upload SB3 models.
-
-#### Installation of `huggingface_sb3`
-
-```sh
pip install huggingface-sb3==2.3.1
-```
-
-#### Upload the model on 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**
-> Link the trained model in the `README.md` file.
-
-> 📝 **Note**
-> [RL-Zoo3](https://stable-baselines3.readthedocs.io/en/master/guide/rl_zoo.html) provides more advanced features to save hyperparameters, generate renderings and metrics. Feel free to try them.
-
-### Get familiar with Weights & Biases
-
-Weights & Biases (W&B) is a tool for machine learning experiment management. With W&B, you can track and compare your experiments, visualize your model training and performance.
-
-#### Installation
-
-You'll need to install both `wand` and `tensorboar`.
-
-```shell
+pip install panda-gym==3.0.7
pip install wandb tensorboard
```
-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.
-
-> ⚠️ **Warning**
-> Make sure to make the run public!
+## Instructions
-### Full workflow with panda-gym
+1. **CartPole Environment avec REINFORCE**:
+ - Exécutez le script `reinforce_cartpole.py` pour entraîner et résoudre l'environnement CartPole-v1 avec l'algorithme REINFORCE.
+ - Visualisez les performances de l'agent en utilisant les graphiques affichés par l'algorithme.
-[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-v2`. The objective is to learn how to reach any point in 3D space by directly controlling the robot's articulations.
-
-#### Installation
-
-```shell
-pip install panda-gym==3.0.7
-```
-
-#### Train, track, and share
+2. **CartPole Environment avec A2C**:
+ - Exécutez le script `a2c_sb3_cartpole.py` pour entraîner et résoudre l'environnement CartPole-v1 avec l'algorithme Advantage Actor-Critic (A2C) de Stable-Baselines3.
+Voici le lien du résultat:
+https://wandb.ai/emilien-paga23/sb3/runs/9lgngdjl
+https://huggingface.co/emipaga/A2C_cartpole
-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.
+3. **PandaReachJointsDense-v2 Environment avec A2C**:
+ - Exécutez le script `a2c_sb3_panda_reach.py` pour entraîner un modèle A2C sur l'environnement PandaReachJointsDense-v2 avec Stable-Baselines3.
+ - Assurez-vous que les résultats de l'entraînement sont partagés dans "Weights & Biases" et que le lien est inclus dans ce fichier README.md.
+ https://huggingface.co/emipaga/A2C_panda_reach/
+ https://wandb.ai/emilien-paga23/panda-gym-training/runs/swh626ru
-> 🛠 **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.
## Contribute
@@ -209,9 +57,4 @@ Updates by Léo Schneider, Emmanuel Dellandréa
MIT
-## Link to huggingface
-https://huggingface.co/emipaga/A2C_cartpole
-
-## Link to wandb
-https://wandb.ai/emilien-paga23/sb3/reports/Untitled-Report--Vmlldzo3MDMxNjk4
diff --git a/a2c_sb3_panda_reach.py b/a2c_sb3_panda_reach.py
index b11b99ea943ea77fe69fefd6b120865c430b51d2..b8c9ad82e412ad561a3465526d60d0b143476e49 100644
--- a/a2c_sb3_panda_reach.py
+++ b/a2c_sb3_panda_reach.py
@@ -37,7 +37,11 @@ env = VecVideoRecorder(
model = A2C(config["policy_type"], env, verbose=1)
-model.learn(total_timesteps=config["total_timesteps"], callback=WandbCallback())
+model.learn(total_timesteps=config["total_timesteps"], callback=WandbCallback(
+ gradient_save_freq=100,
+ model_save_path=f"models/{run.id}",
+ verbose=2,
+ ),)
model.save("a2c_panda_reach_model")