Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Laz4rz/hf-LunarLander-1-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Laz4rz/hf-LunarLander-1-ppo with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Laz4rz/hf-LunarLander-1-ppo", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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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...
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```
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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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Follow to eval the agent locally:
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```python
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repo_id = "Laz4rz/hf-LunarLander-1-ppo" # The repo_id
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filename = "ppo-LunarLander-v2.zip" # The model filename.zip
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checkpoint = load_from_hub(repo_id, filename)
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model = PPO.load(checkpoint)
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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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...
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```
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