Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use JuanMa360/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use JuanMa360/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="JuanMa360/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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README.md
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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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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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repo_id = "JuanMa360/ppo-LunarLander-v2"
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filename = "ppo-LunarLander-v2.zip"
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# When the model was trained on Python 3.8 the pickle protocol is 5
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# But Python 3.6, 3.7 use protocol 4
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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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```python
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!pip install shimmy
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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repo_id = "JuanMa360/ppo-LunarLander-v2"
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filename = "ppo-LunarLander-v2.zip"
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# When the model was trained on Python 3.8 the pickle protocol is 5
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# But Python 3.6, 3.7 use protocol 4
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