Update README.md

README.md

@@ -26,12 +26,59 @@ 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 ...
-from huggingface_sb3 import load_from_hub
+import gymnasium as gym
+from pyvirtualdisplay import Display
+from huggingface_sb3 import load_from_hub, package_to_hub
+from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the 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
 
+virtual_display = Display(visible=0, size=(1400, 900))
+virtual_display.start()
+
+env = gym.make("LunarLander-v2")
+
+observation, info = env.reset()
+
+for _ in range(20):
+  action = env.action_space.sample()
+  print("Action taken:", action)
+
+  observation, reward, terminated, truncated, info = env.step(action)
+
+  if terminated or truncated:
+      # Reset the environment
+      print("Environment is reset")
+      observation, info = env.reset()
+
+
+env.reset()
+
+print("_____OBSERVATION SPACE_____ \n")
+print("Observation Space Shape", env.observation_space.shape)
+print("Sample observation", env.observation_space.sample())
+
+print("\n _____ACTION SPACE_____ \n")
+print("Action Space Shape", env.action_space.n)
+print("Action Space Sample", env.action_space.sample()) # Take a random action
+
+env = make_vec_env('LunarLander-v2', n_envs=16)
+
+model = PPO(policy = 'MlpPolicy', env = env, n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1)
+
+
+model.learn(total_timesteps=1000000)
+
+model_name = "ppo-LunarLander-v2"
+model.save(model_name)
+
+eval_env = Monitor(gym.make("LunarLander-v2", render_mode='rgb_array'))
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
 ...
 ```