README.md

-# 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.
 
 ## 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.
-The last commit is due before 11:59 pm on Monday, February 13, 2023. Subsequent commits will not be considered.
+This work must be done individually. The expected output is a repository named `hands-on-rl` on https://gitlab.ec-lyon.fr. 
+
+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**
 > 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
 
 ## 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
 
+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
-pip install gym==0.21
+pip install gymnasium
 ```
 
-Install also pyglet for the rendering.
-
 ```sh
-pip install pyglet==1.5.27
+pip install "gymnasium[classic-control]"
 ```
 
+
 ### 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
-import gym
+import gymnasium as gym
 
 # Create the environment
-env = gym.make("CartPole-v1")
+env = gym.make("CartPole-v1", render_mode="human")
 
 # Reset the environment and get the initial observation
 observation = env.reset()
@@ -49,9 +67,14 @@ for _ in range(100):
     # 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, done, info = env.step(action)
+    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
@@ -74,12 +97,21 @@ Repeat 500 times:
     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
+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**
-> 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
 
@@ -92,12 +124,11 @@ Stable-Baselines3 (SB3) is a high-level RL library that provides various algorit
 #### Installation
 
 ```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
 
 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
 #### Installation of `huggingface_sb3`
 
 ```sh
-pip install huggingface_sb3
+pip install huggingface-sb3
 ```
 
 #### 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**
 > 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
 
 #### Installation
 
+You'll need to install `wand`.
+
 ```shell
 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.
 
 > ⚠️ **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
 
-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
 
 ```shell
-pip install panda_gym==2.0.0
+pip install panda-gym==3.0.7
 ```
 
 #### 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**
 > 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.
 
 Quentin Gallouédec
 
+Updates by Bruno Machado, Léo Schneider, Emmanuel Dellandréa
+
 ## License
 
 MIT