Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use codeSpaghetti/PPO-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use codeSpaghetti/PPO-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="codeSpaghetti/PPO-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): a947d36
Update README.md
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README.md
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@@ -26,12 +26,21 @@ 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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...
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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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```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 = "osbm/ppo-LunarLander-v2-2"
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filename = "ppo-LunarLander-v2.zip"
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custom_objects = {
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"learning_rate": 0.0,
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"lr_schedule": lambda _: 0.0,
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"clip_range": lambda _: 0.0,
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}
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checkpoint = load_from_hub(repo_id, filename)
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model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
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...
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```
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