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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: 266.93 +/- 24.72 |
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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** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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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="kingabzpro/Moonman-Lunar-Landing-v2", filename="Moonman-Lunar-Landing-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 = eval_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 = eval_env.step(action) |
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eval_env.render() |
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if done: |
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obs = eval_env.reset() |
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eval_env.close() |
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``` |
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