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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: 255.38 +/- 18.53 |
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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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TODO: Add your code |
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```python |
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import gymnasium as gym |
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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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env = make_vec_env('LunarLander-v2', n_envs=16) |
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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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model.learn(total_timesteps=1000000) |
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model_name = "ppo-LunarLander-v2" |
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model.save(model_name) |
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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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``` |
