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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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- metrics: |
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- type: mean_reward |
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value: 245.74 +/- 18.06 |
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name: mean_reward |
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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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--- |
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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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import 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.evaluation import evaluate_policy |
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from stable_baselines3.common.env_util import make_vec_env |
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env = make_vec_env('LunarLander-v2', n_envs=16) |
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model = PPO('MlpPolicy', env, verbose=1) |
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model.learn(total_timesteps=5 * 10**5) |
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eval_env = gym.make('LunarLander-v2') |
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mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=10, deterministic=True) |
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print(f"Reward mean: {mean_reward:.2f}, Reward STD: {std_reward:.2f}") |
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``` |
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