Update README.md

README.md

@@ -25,13 +25,90 @@ 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
+## Usage 
+code was done with gym env and stable-basline3 libraray 
 
 
 ```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+!apt install swig cmake
+!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit1/requirements-unit1.txt
+!sudo apt-get update
+!apt install python3-opengl
+!apt install ffmpeg
+!apt install xvfb
+!pip3 install pyvirtualdisplay
+# restart colab
+import os
+os.kill(os.getpid(), 9)
+#display
+from pyvirtualdisplay import Display
 
+virtual_display = Display(visible=0, size=(1400, 900))
+virtual_display.start()
+# import libraries
+import gymnasium
+
+from huggingface_sb3 import load_from_hub, package_to_hub
+from huggingface_hub import (
+    notebook_login,
+) 
+
+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
+
+# Create environment
+env = gym.make('LunarLander-v2')
+
+
+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,
+)
+# Train the agent
+model.learn(total_timesteps=1000000)
+
+# Save the model
+model_name = "ppo-LunarLander-v2"
+model.save(model_name)
+
+#evaluate model
+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:.2f} +/- {std_reward}")
+
+# create a video (for colab)
+import gym
+from stable_baselines3 import PPO
+from IPython.display import Video, display
+import os
+
+env = gym.make('LunarLander-v2')
+
+model_name = "ppo-LunarLander-v2"
+model = PPO.load(model_name)
+
+def record_video(env, model, video_length=500, prefix="ppo-lunarlander"):
+    env = gym.wrappers.RecordVideo(env, video_folder=prefix, episode_trigger=lambda x: x == 0)
+    obs = env.reset()
+    for _ in range(video_length):
+        action, _ = model.predict(obs)
+        obs, _, done, _ = env.step(action)
+        if done:
+            obs = env.reset()
+    env.close()
+
+record_video(env, model, video_length=500, prefix="ppo-lunarlander")
+
+video_path = "ppo-lunarlander/rl-video-episode-0.mp4"
+display(Video(video_path))
 ...
 ```