Update README.md

README.md

@@ -4,4 +4,55 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 ---
-# TODO: Fill this model card
+# ppo-Walker2DBulletEnv-v0
+
+This is a pre-trained model of a PPO agent playing AntBulletEnv-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
+
+<video src="https://huggingface.co/ThomasSimonini/ppo-AntBulletEnv-v0/resolve/main/output.mp4" controls autoplay loop></video>
+
+### Usage (with Stable-baselines3)
+Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
+```
+pip install stable-baselines3
+pip install huggingface_sb3
+```
+
+Then, you can use the model like this:
+
+```python
+
+import gym
+import pybullet_envs
+
+from huggingface_sb3 import load_from_hub
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+from stable_baselines3.common.evaluation import evaluate_policy
+
+# Retrieve the model from the hub
+## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
+## filename = name of the model zip file from the repository
+repo_id = "ThomasSimonini/ppo-AntBulletEnv-v0"
+checkpoint = load_from_hub(repo_id = repo_id, filename="ppo-AntBulletEnv-v0.zip")
+model = PPO.load(checkpoint)
+
+# Load the saved statistics
+stats_path = load_from_hub(repo_id = repo_id, filename="vec_normalize.pkl")
+
+eval_env = DummyVecEnv([lambda: gym.make("AntBulletEnv-v0")])
+eval_env = VecNormalize.load(stats_path, eval_env)
+#  do not update them at test time
+eval_env.training = False
+# reward normalization is not needed at test time
+eval_env.norm_reward = False
+
+from stable_baselines3.common.evaluation import evaluate_policy
+
+mean_reward, std_reward = evaluate_policy(model, eval_env)
+print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
+
+```
+
+### Evaluation Results
+Mean_reward: 3547.01 +/- 33.32