diff --git a/README.md b/README.md
index 88a21a53621c4dcfce703ae114e9afa5165e661e..da972f40c01ef29f8633c76b1b3c11d805bfad77 100644
--- a/README.md
+++ b/README.md
@@ -2,226 +2,23 @@
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.
-
-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.
-
-## 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
-```
-
-```sh
-pip install numpy==1.26.4
-```
-
-If needed
-
-```sh
-pip install pygame==2.5.2
-```
-
-```sh
-pip install PyQt5
-```
-
-```sh
-pip install opencv-python
-```
-
-### 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()
-```
+### Jules Coulon
## 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
-Save the model weights
-```
-
-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`. 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**
-> 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
-
-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 stable-baselines3[extra]
-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 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!
-
-### Full workflow with panda-gym
-
-[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==3.0.7
-```
-
-#### Train, track, and share
-
-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.
-
-## Contribute
-
-This tutorial may contain errors, inaccuracies, typos or areas for improvement. Feel free to contribute to its improvement by opening an issue.
+Train the cartpole with the reinforce method with 500 episodes of 500 iterations maximum each give this reward and this loss.
+We can find the code in this [python file](reinforce_cartpole.py).
+
+
-## Author
+This modele is evaluated on [evaluate_reinforce_cartpole] (evaluate_reinforce_cartpole.py) and gives an achievement of 100%.
-Quentin Gallouédec
+### CartPole with SB3
-Updates by Bruno Machado, Léo Schneider, Emmanuel Dellandréa
+The cartpole is trained with SB3 this time with the A2C model on [a2c_sb3_cartpole](a2c_sb3_cartpole.py).
+We can find the modele on huggingface on this [link](https://huggingface.co/JulesCoulon/A2C_CartPole/tree/main) and the train on this [link](https://wandb.ai/julescoulon10-centrale-lyon/cartpole/runs/axqnijqu?nw=nwuserjulescoulon10) with wandb.
-## License
+### Panda Reach with SB3
-MIT
+The Pandareach is trained with SB3 with the A2C model with 500000 timesteps on [a2c_sb3_pand_reach](a2c_sb3_panda_reach.py).
+We can find the modele on huggingface on this [link](https://huggingface.co/JulesCoulon/A2C_CartPole/tree/main) and the train on this [link](https://wandb.ai/julescoulon10-centrale-lyon/cartpole/runs/axqnijqu?nw=nwuserjulescoulon10) with wandb.
\ No newline at end of file
diff --git a/a2c_sb3_cartpole.py b/a2c_sb3_cartpole.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bcac27d129af18086f74677fb704f7bcac8d06e
--- /dev/null
+++ b/a2c_sb3_cartpole.py
@@ -0,0 +1,41 @@
+import gym
+from stable_baselines3 import A2C
+import wandb
+from wandb.integration.sb3 import WandbCallback
+from stable_baselines3.common.env_util import make_vec_env
+
+# start a new wandb run to track this script
+config = {
+ "policy_type": "MlpPolicy",
+ "total_timesteps": 25000,
+ "env_name": "CartPole-v1",
+}
+
+run = wandb.init(
+ project="cartpole",
+ config=config,
+ sync_tensorboard=True,
+ monitor_gym=True,
+ save_code=True,
+)
+env = make_vec_env("CartPole-v1", n_envs=4)
+
+# Train the model
+model = A2C(config["policy_type"], env, verbose=1, tensorboard_log=f"runs/{run.id}")
+model.learn(
+ total_timesteps=config["total_timesteps"],
+ callback=WandbCallback()
+)
+
+run.finish()
+
+if False:
+ from huggingface_sb3 import push_to_hub
+ from huggingface_hub import login
+
+ login(token="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")
+ push_to_hub(
+ repo_id="JulesCoulon/A2C_CartPole",
+ filename="a2c_cartpole.zip",
+ commit_message="Added A2C model for CartPole with Stable Baselines3",
+ )
\ No newline at end of file
diff --git a/a2c_sb3_panda_reach.py b/a2c_sb3_panda_reach.py
new file mode 100644
index 0000000000000000000000000000000000000000..770029746bd4705cb9081b681e9d7633612ece61
--- /dev/null
+++ b/a2c_sb3_panda_reach.py
@@ -0,0 +1,38 @@
+import gym
+import panda_gym
+from stable_baselines3 import A2C
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import DummyVecEnv
+import wandb
+from wandb.integration.sb3 import WandbCallback
+
+
+config = {
+ "policy_type": "MultiInputPolicy",
+ "total_timesteps": 500000,
+ "env_name": "PandaReachJointsDense-v3",
+}
+
+run = wandb.init(
+ project="pandareach",
+ config=config,
+ sync_tensorboard=True,
+ monitor_gym=True,
+ save_code=True,
+)
+
+def make_env():
+ env = gym.make(config["env_name"])
+ env = Monitor(env) # record stats such as returns
+ return env
+
+env = DummyVecEnv([make_env])
+env = gym.make("PandaReachJointsDense-v3")
+model = A2C(config["policy_type"], env, verbose=1, tensorboard_log=f"runs/{run.id}")
+model.learn(
+ total_timesteps=config["total_timesteps"],
+ callback=WandbCallback(
+ )
+)
+
+run.finish()
diff --git a/evaluate_reinforce_cartpole.py b/evaluate_reinforce_cartpole.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb1f7e24b76fc7b82a80d03ab2af544a92a3b03e
--- /dev/null
+++ b/evaluate_reinforce_cartpole.py
@@ -0,0 +1,53 @@
+from tqdm import tqdm
+import torch
+import gym
+from torch.distributions import Categorical
+import torch.nn as nn
+
+class SimpleNN(nn.Module):
+ def __init__(self):
+ super(SimpleNN, self).__init__()
+
+ # Première couche entièrement connectée avec 128 unités et activation ReLU
+ self.fc1 = nn.Linear(4, 128) # sortie de l'observation
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(0.25)
+
+ # Deuxième couche entièrement connectée suivie d'une activation Softmax
+ self.fc2 = nn.Linear(128, 2) # 2 action space
+ self.softmax = nn.Softmax(dim=0)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.relu(x)
+ x = self.dropout(x)
+ x = self.fc2(x)
+ x = self.softmax(x)
+ return x
+
+
+model = SimpleNN()
+model.load_state_dict(torch.load("reinforce_cartpole.pth"))
+model.eval()
+# Create the environment
+env = gym.make("CartPole-v1", render_mode=None)
+
+achievement = 0
+
+for ep in tqdm(range(100)):
+ observation = env.reset()[0]
+ terminated = False
+ for id in range(500):
+ prob = model(torch.tensor(observation))
+ # Choose the action with the highest probability
+ action = torch.argmax(prob)
+ observation, reward, terminated, truncated, info = env.step(action.item())
+ if terminated:
+ print("Episode terminated at step ", id)
+ break
+ if not terminated:
+ print("Episode terminated at step ", id)
+ achievement += 1
+
+print("Achievement rate : ", achievement, "%")
+env.close()
\ No newline at end of file
diff --git a/reinforce_cartpole.py b/reinforce_cartpole.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea72c1e615b1142fecfa5753e9800d7e0ecffe5
--- /dev/null
+++ b/reinforce_cartpole.py
@@ -0,0 +1,102 @@
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions import Categorical
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+
+
+env = gym.make("CartPole-v1", render_mode=None)
+# Définition du modèle
+class SimpleNN(nn.Module):
+ def __init__(self):
+ super(SimpleNN, self).__init__()
+
+ # Première couche entièrement connectée avec 128 unités et activation ReLU
+ self.fc1 = nn.Linear(4, 128) # sortie de l'observation
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(0.25)
+
+ # Deuxième couche entièrement connectée suivie d'une activation Softmax
+ self.fc2 = nn.Linear(128, 2) # 2 action space
+ self.softmax = nn.Softmax(dim=0)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.relu(x)
+ x = self.dropout(x)
+ x = self.fc2(x)
+ x = self.softmax(x)
+ return x
+
+# Instancier le modèle
+model = SimpleNN()
+
+gamma = 0.99
+# Définir l'optimiseur et la fonction de perte
+optimizer = optim.Adam(model.parameters(), lr=5*10**-3)
+criterion = nn.CrossEntropyLoss() # La CrossEntropyLoss inclut Softmax, donc pas besoin de le redéfinir
+
+# Affichage du modèle
+print(model)
+
+nb_episode = 500
+max_episode_steps = 500
+total_rewards = []
+total_loss = []
+for ep in range(nb_episode):
+ observation = env.reset()[0]
+ buffer = torch.zeros(max_episode_steps + 1)
+ probs = torch.zeros(max_episode_steps + 1)
+ done = False
+ id = 0
+ terminated = False
+ while not(terminated) and id < max_episode_steps:
+ prob = model(torch.tensor(observation))
+ m = Categorical(prob)
+ action = m.sample()
+ probs[id] = prob[action]
+ observation, reward, terminated, truncated, info = env.step(action.item())
+ for i in range(id+1):
+ buffer[i] += reward * gamma**(i-1)
+ id += 1
+ # env.render()
+ total_rewards.append(id)
+ probs = probs[:id]
+ buffer = buffer[:id]
+ F.normalize(buffer, dim=0)
+
+ # Compute loss
+ logs = torch.log10(probs)
+ loss = - torch.sum(torch.mul(logs, buffer))
+ total_loss.append(loss)
+
+ # Print progress
+ if (ep + 1) % 50 == 0:
+ print("Épisode : {} / {}".format(ep + 1, nb_episode))
+ print("Reward : ", id)
+ print("Loss : ", round(loss.item(), 2))
+
+
+ # Perform gradient descent to update neural network
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+
+# Plot the evolution of learning : reward and loss
+# Reward
+plt.plot(total_rewards)
+plt.xlabel('Épisode')
+plt.ylabel('Reward')
+plt.title("Évolution du reward en fonction de l'épisode")
+plt.show()
+
+# Loss
+total_losses = [loss.item() for loss in total_loss]
+plt.plot(total_losses)
+plt.xlabel('Épisode')
+plt.ylabel('Loss')
+plt.title("Évolution du loss en fonction de l'épisode")
+plt.show()