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--- |
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library_name: stable-baselines3 |
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tags: |
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- LunarLander-v2 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: PPO |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: LunarLander-v2 |
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type: LunarLander-v2 |
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metrics: |
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- type: mean_reward |
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value: 261.85 +/- 46.42 |
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name: mean_reward |
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verified: false |
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--- |
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## My First RL Project |
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# **PPO** Agent playing **LunarLander-v2** |
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This is a trained model of a **PPO** agent playing **LunarLander-v2** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage |
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code was done with gym env and stable-basline3 libraray |
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```python |
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#Dependencies and stuff |
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!apt install swig cmake |
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!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit1/requirements-unit1.txt |
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!sudo apt-get update |
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!apt install python3-opengl |
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!apt install ffmpeg |
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!apt install xvfb |
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!pip3 install pyvirtualdisplay |
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# restart colab |
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import os |
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os.kill(os.getpid(), 9) |
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#display |
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from pyvirtualdisplay import Display |
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virtual_display = Display(visible=0, size=(1400, 900)) |
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virtual_display.start() |
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# import libraries |
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import gymnasium |
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from huggingface_sb3 import load_from_hub, package_to_hub |
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from huggingface_hub import ( |
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notebook_login, |
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) |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.env_util import make_vec_env |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from stable_baselines3.common.monitor import Monitor |
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# Create environment |
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env = gym.make('LunarLander-v2') |
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#Define PPO |
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model = PPO( |
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policy="MlpPolicy", |
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env=env, |
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n_steps=1024, |
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batch_size=64, |
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n_epochs=4, |
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gamma=0.999, |
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gae_lambda=0.98, |
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ent_coef=0.01, |
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verbose=1, |
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) |
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# Train the agent |
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model.learn(total_timesteps=1000000) |
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# Save the model |
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model_name = "ppo-LunarLander-v2" |
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model.save(model_name) |
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#evaluate model |
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eval_env = Monitor(gym.make("LunarLander-v2")) |
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mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) |
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print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") |
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# create a video (for colab) |
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from stable_baselines3 import PPO |
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from IPython.display import Video, display |
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import os |
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env = gym.make('LunarLander-v2') |
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model_name = "ppo-LunarLander-v2" |
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model = PPO.load(model_name) |
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def record_video(env, model, video_length=500, prefix="ppo-lunarlander"): |
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env = gym.wrappers.RecordVideo(env, video_folder=prefix, episode_trigger=lambda x: x == 0) |
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obs = env.reset() |
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for _ in range(video_length): |
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action, _ = model.predict(obs) |
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obs, _, done, _ = env.step(action) |
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if done: |
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obs = env.reset() |
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env.close() |
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record_video(env, model, video_length=500, prefix="ppo-lunarlander") |
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video_path = "ppo-lunarlander/rl-video-episode-0.mp4" |
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display(Video(video_path)) |
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... |
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``` |