Finito

README.md

@@ -25,4 +25,25 @@ 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 track the learning phase : [WandB tacking](https://wandb.ai/maximecerise-ecl/cartpole-a2c?nw=nwusermaximecerise)
+Wandb has been set up to track the learning phase : [Report here](reports/A2C_CARTPOLE_REPORT.pdf)
+
+Preview : 
+<video controls src="videos/preview.mp4" title="Title"></video>
+
+
+
+### 3. Panda Reach 
+
+Stable-Baselines3 package to train A2C model on the PandaReachJointsDense-v3 environment. 500k timesteps.
+
+
+#### To run [a2c_sb3_panda_reach.py](a2c_sb3_panda_reach.py) : 
+
+<code> pip install -r "requirement_reach.txt </code>
+<code> python a2c_sb3_panda_reach.py </code>
+
+- <b>Code:</b> [a2c_sb3_panda_reach.py](a2c_sb3_panda_reach.py)
+- <b>Hugging face :</b> [Here](https://huggingface.co/MaximeCerise/a2c-reach)
+- <b>WandB's report : </b> [a2c_reach_panda_report](<reports/A2C_reach_panda_report .pdf>)
+- <b>Preview :</b>
+<video controls src="videos/preview_reach.mp4" title="Title"></video>

a2c_sb3_panda_reach.py

+import gymnasium as gym
+import panda_gym
+import wandb
+from wandb.integration.sb3 import WandbCallback
+from huggingface_sb3 import load_from_hub, package_to_hub
+from huggingface_hub import login
+from stable_baselines3 import A2C
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import DummyVecEnv, VecVideoRecorder
+def a2c_reach():
+    config = {
+        "policy_type": "MultiInputPolicy",
+        "total_timesteps": 500000,
+        "env_name": "PandaReachJointsDense-v3",
+    }
+
+    model_wb_name="a2c-reach"
+
+    env_id=config["env_name"]
+    repo_user = "MaximeCerise"
+    model_push_name = "a2c-reach"
+    commit_message = "v7"
+
+    run = wandb.init(
+        name=model_wb_name,
+        project="a2c-reach",
+        config=config,
+        sync_tensorboard=True,
+        monitor_gym=True,
+        save_code=True,
+    )
+
+
+    env = gym.make(config["env_name"], render_mode="rgb_array")
+    env = Monitor(env)
+    env = DummyVecEnv([lambda:env])
+    #env = VecVideoRecorder(
+    #   env,
+    #  f"wandb_data/videos/{run.id}",
+    # record_video_trigger=lambda x: x % 50000 == 0,
+        #video_length=100,
+
+    #)
+    env = VecVideoRecorder(
+        env,
+        video_folder="videos", 
+        record_video_trigger=lambda x: x == 0, 
+        video_length=3000, 
+        name_prefix="preview"  
+    )
+
+
+    model = A2C(config["policy_type"], env, verbose=1, tensorboard_log=f"wandb_data/runs/{run.id}")
+    model.learn(config["total_timesteps"], 
+                progress_bar=True,
+                callback=WandbCallback(
+                                        gradient_save_freq=100,
+                                        model_save_path=f"wandb_data/models/{run.id}",
+                                        verbose=2,
+                                    ))
+    run.finish()
+
+
+    model.save(f"models/{model_push_name}")
+
+
+    mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=100, deterministic=True)
+    print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+
+
+    login('')
+
+    eval_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])
+
+    package_to_hub(model=model,
+                model_name=model_push_name,
+                model_architecture="A2C",
+                env_id=env_id,
+                eval_env=eval_env,
+                repo_id=f"{repo_user}/{model_push_name}",
+                commit_message=commit_message)
+
+    checkpoint = load_from_hub(f"{repo_user}/{model_push_name}", f"{model_push_name}.zip")
+
+    model = A2C.load(checkpoint, print_system_info=True)
+
+    obs = env.reset()
+    for i in range(2000):
+        action, _state = model.predict(obs, deterministic=True)
+        obs, reward, done, info = env.step(action)
+        env.render("human")
+
+if __name__ == "__main__":
+    a2c_reach()
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requirements_reach.txt

+panda-gym==3.0.7
+stable-baselines3
+wandb
+huggingface-hub
+gymnasium
+huggingface_sb3
+tensorboard
+moviepy
+stable-baselines3[extra]
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