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--- |
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tags: |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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--- |
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# "Beyko7/ppo-LunarLander-v2" |
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This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. |
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### Usage (with Stable-baselines3) |
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Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: |
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``` |
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pip install stable-baselines3 |
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pip install huggingface_sb3 |
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``` |
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Then, you can use the model like this: |
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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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# Retrieve the model from the hub |
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## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) |
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## filename = name of the model zip file from the repository |
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checkpoint = load_from_hub(repo_id="Beyko7/ppo-LunarLander-v2", filename="LunarLander-v2.zip") |
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model = PPO.load(checkpoint) |
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# Evaluate the agent |
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eval_env = 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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# Watch the agent play |
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obs = env.reset() |
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for i in range(1000): |
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action, _state = model.predict(obs) |
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obs, reward, done, info = env.step(action) |
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env.render() |
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if done: |
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obs = env.reset() |
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env.close() |
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``` |
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### Evaluation Results |
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Mean_reward: 248.30 +/- 23.32882124373712 |
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--- |
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