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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: 263.26 +/- 19.25 |
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name: mean_reward |
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verified: false |
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--- |
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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 (with Stable-baselines3) |
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```python |
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# !pip gymnasium huggingface-sb3 stable_baselines3[extra] |
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import gymnasium as gym |
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from huggingface_sb3 import load_from_hub |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.vec_env import DummyVecEnv |
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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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repo_id = "VinayHajare/ppo-LunarLander-v2" |
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filename = "ppo-LunarLander-v2.zip" |
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eval_env = gym.make("LunarLander-v2", render_mode="human") |
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checkpoint = load_from_hub(repo_id, filename) |
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model = PPO.load(checkpoint,print_system_info=True) |
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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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# Enjoy trained agent |
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observation, info = eval_env.reset() |
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for _ in range(1000): |
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action, _states = model.predict(observation, deterministic=True) |
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observation, rewards, terminated, truncated, info = eval_env.step(action) |
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eval_env.render() |
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``` |
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