Add code

README.md

@@ -20,9 +20,58 @@ model-index:
       type: LunarLander-v2
 ---
 
-  # **PPO** Agent playing **LunarLander-v2**
+# **PPO** Agent playing **LunarLander-v2**
   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
+## Usage (with Stable-baselines3)
+
+```python
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.callbacks import EvalCallback
+
+# Create the environment
+env_id = "LunarLander-v2"
+n_envs = 16
+env = make_vec_env(env_id, n_envs=n_envs)
+
+# Create the evaluation envs
+eval_envs = make_vec_env(env_id, n_envs=5)
+
+# Adjust evaluation interval depending on the number of envs
+eval_freq = int(1e5)
+eval_freq = max(eval_freq // n_envs, 1)
+
+# Create evaluation callback to save best model
+# and monitor agent performance
+eval_callback = EvalCallback(
+    eval_envs,
+    best_model_save_path="./logs/",
+    eval_freq=eval_freq,
+    n_eval_episodes=10,
+)
+
+# Instantiate the agent
+# Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
+model = PPO(
+    "MlpPolicy",
+    env,
+    n_steps=1024,
+    batch_size=64,
+    gae_lambda=0.98,
+    gamma=0.999,
+    n_epochs=4,
+    ent_coef=0.01,
+    verbose=1,
+)
+
+# Train the agent (you can kill it before using ctrl+c)
+try:
+    model.learn(total_timesteps=int(5e6), callback=eval_callback)
+except KeyboardInterrupt:
+    pass
+
+# Load best model
+model = PPO.load("logs/best_model.zip")
+```