README.md

@@ -25,6 +25,4 @@ We finally have an evaluation with 100% of sucess:
 
 Here we set up a complete pipeline to solve Cartpole environment with A2C algorithm.
 
-Wandb has been set up to follow the learning phase. 
-https://wandb.ai/maximecerise-ecl/cartpole-a2c
-![alt text](saves/rollout.png)
+Wandb has been set up to track the learning phase : [WandB tacking](https://wandb.ai/maximecerise-ecl/cartpole-a2c?nw=nwusermaximecerise)

a2c_sb3_cartpole.py

 import wandb
 import gymnasium as gym
+import numpy as np
 from stable_baselines3 import A2C
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.vec_env import DummyVecEnv, VecVideoRecorder
 from huggingface_sb3 import package_to_hub
 
-
+def a2c_sb3():
     env = gym.make("CartPole-v1", render_mode="rgb_array")
     env = Monitor(env)
     env = DummyVecEnv([lambda: env])
 
-
     wandb.init(
         entity="maximecerise-ecl",
         project="cartpole-a2c",
@@ -21,15 +21,57 @@ wandb.init(
 
 
     model = A2C("MlpPolicy", env, verbose=1, tensorboard_log="./a2c_tensorboard/")
-model.learn(total_timesteps=500000)
+    model.learn(total_timesteps=300000)
 
 
     model.save("a2c_cartpole")
     env.close()
 
+
     eval_env = gym.make("CartPole-v1", render_mode="rgb_array")
     eval_env = Monitor(eval_env)
     eval_env = DummyVecEnv([lambda: eval_env])
+
+    success_count = 0
+    num_episodes = 100
+    scores = []
+
+    for episode in range(num_episodes):
+        obs = eval_env.reset()
+        done = False
+        episode_reward = 0
+
+        while not done:
+            action, _ = model.predict(obs)
+            obs, reward, done, info = eval_env.step(action)
+            done = done[0]  # Extraction de la valeur booléenne
+            episode_reward += reward
+
+        scores.append(episode_reward)
+        if episode_reward >= 200:
+            success_count += 1
+
+
+        wandb.log({
+            "episode": episode,
+            "episode_reward": episode_reward,
+            "success_rate (%)": success_count / (episode + 1) * 100
+        })
+
+
+    success_rate = success_count / num_episodes * 100
+    avg_score = np.mean(scores)
+
+
+    wandb.log({
+        "final_success_rate (%)": success_rate,
+        "final_average_score": avg_score
+    })
+
+    print(f"Taux de succès du modèle A2C sur {num_episodes} épisodes : {success_rate:.2f}%")
+    print(f"Score moyen : {avg_score:.2f}")
+
+
     video_folder = "./videos/"
     eval_env = VecVideoRecorder(eval_env, video_folder, record_video_trigger=lambda x: x == 0, video_length=1000)
 
@@ -40,6 +82,7 @@ for _ in range(1000):
 
     eval_env.close()
 
+
     package_to_hub(
         model=model,
         model_name="a2c_cartpole",
@@ -47,6 +90,10 @@ package_to_hub(
         env_id="CartPole-v1",
         eval_env=eval_env,
         repo_id="MaximeCerise/a2c_cartpole",
-    commit_message="add a2c"
+        commit_message="add a2c with evaluation"
     )
+
+
     wandb.finish()
+if __name__ == "__main__":
+    a2c_sb3()
\ No newline at end of file

evaluate_reinforce_cartpole.py

-import gym
+import gymnasium as gym
 import torch
 import numpy as np
 import torch