Code added

README.md

@@ -24,5 +24,44 @@ model-index:
   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
+  import gym
+from stable_baselines3 import PPO
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.env_util import make_vec_env
+
+# Create a vectorized environment of 16 parallel environments
+env = make_vec_env("LunarLander-v2", n_envs=16)
+
+# Optimizaed Hyperparameters
+model = PPO(
+    "MlpPolicy",
+    env=env,
+    n_steps=655,
+    batch_size=32,
+    n_epochs=8,
+    gamma=0.998,
+    gae_lambda=0.98,
+    ent_coef=0.01,
+    verbose=1,
+)
+
+# Train it for 500,000 timesteps
+model.learn(total_timesteps=int(5e6))
+
+# Create a new environment for evaluation
+eval_env = gym.make("LunarLander-v2")
+
+# Evaluate the model with 10 evaluation episodes and deterministic=True
+mean_reward, std_reward = evaluate_policy(
+    model, eval_env, n_eval_episodes=10, deterministic=True
+)
+
+# Print the results
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+
+#>>> mean_reward=254.56 +/- 18.45056958672337
+
+
+  ```