Update README.md

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 254.93 +/- 22.52
+      value: 249.81 +/- 23.73
       name: mean_reward
       verified: false
 ---
@@ -26,12 +26,42 @@ This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
-TODO: Add your code
+```python
+from stable_baselines3 import PPO
+from stable_baselines3.common.envs import LunarLander
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
 
+# Create the LunarLander environment
+env = LunarLander()
 
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+# Vectorize the environment for parallel training
+vec_env = make_vec_env('LunarLander-v2', n_envs=16)
+
+# Instantiate the PPO agent
+model = PPO("MlpPolicy", vec_env, verbose=1)
+
+# Train the agent
+model.learn(total_timesteps=int(2e5))
 
-...
+# Evaluate the trained agent
+eval_env = LunarLander()
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+
+# Save the trained model
+model_name = "ppo-LunarLander-v2"
+model.save(model_name)
+
+# Package and upload the model to the Hub
+from huggingface_sb3 import package_to_hub
+package_to_hub(model=model,
+               model_name=model_name,
+               model_architecture="PPO",
+               env_id="LunarLander-v2",
+               eval_env=eval_env,
+               repo_id="your-username/ppo-LunarLander-v2",
+               commit_message="Upload PPO LunarLander-v2 trained agent")
 ```
+
+Ensure to replace `"your-username"` with your Hugging Face username.