Update README.md

README.md

@@ -23,15 +23,48 @@ model-index:
 
 # **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).
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) 
+
+This model is trained with the help of [Deep RL Course by HuggingFace](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
 
 ## Usage (with Stable-baselines3)
-TODO: Add your code
+```python
+# necessary libraries
+import gymnasium as gym
 
+from huggingface_sb3 import load_from_hub, package_to_hub
+from huggingface_hub import (
+    notebook_login,
+)
 
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+
+# Step 1 : Create an environment
+env = gym.make("LunarLander-v2")
+observation,info = env.reset() # initialize the environment
+
+# Step 2 : Create the model
+model = PPO(
+    policy = "MlpPolicy", # Multiple Layer Perceptron Policy
+    env = env,
+    n_steps = 1024,
+    batch_size = 64,
+    n_epochs = 5,
+    gamma = 0.995,
+    gae_lambda = 0.98,
+    ent_coef = 0.0001,
+    clip_range = 0.1,
+    verbose = 1
+)
+
+# Step 3 : Train the model
+model.learn(total_timesteps=2500000,progress_bar = True)
 
-...
+# Step 4 : Evaluation
+eval_env = Monitor(gym.make("LunarLander-v2"))
+mean_reward,std_reward = evaluate_policy(model,eval_env,n_eval_episodes = 10 ,deterministic=True)
+print(f"Mean reward : {mean_reward} +/- {std_reward}")
 ```