Update README.md

README.md

@@ -24,5 +24,39 @@ model-index:
   This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
   
   ## Usage (with Stable-baselines3)
-  TODO: Add your code
+  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
+
+from huggingface_sb3 import load_from_hub
+from stable_baselines3 import PPO
+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
+checkpoint = load_from_hub(repo_id="kingabzpro/Full-Force-MountainCar-v0", filename="Full-Force-MountainCar-v0.zip")
+model = PPO.load(checkpoint)
+
+# Evaluate the agent
+eval_env = gym.make('MountainCar-v0')
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+ 
+# Watch the agent play
+obs = eval_env.reset()
+for i in range(1000):
+    action, _state = model.predict(obs)
+    obs, reward, done, info = eval_env.step(action)
+    eval_env.render()
+    if done:
+        obs = eval_env.reset()
+eval_env.close()
+```
+