Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use LibRust/lunar_lander_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use LibRust/lunar_lander_model with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="LibRust/lunar_lander_model", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download ppo-LunarLander-v2/policy.optimizer.pth from LibRust/lunar_lander_model: direct link, hf CLI and curl.
- Browser
- Download file 88.7 kB
-
https://huggingface.co/LibRust/lunar_lander_model/resolve/main/ppo-LunarLander-v2/policy.optimizer.pth
- Command line
-
hf download hf://LibRust/lunar_lander_model/ppo-LunarLander-v2/policy.optimizer.pth
-
curl -L -o policy.optimizer.pth https://huggingface.co/LibRust/lunar_lander_model/resolve/main/ppo-LunarLander-v2/policy.optimizer.pth
88.7 kB
- Xet hash:
- d4ccd711505a04706592909054a10cbab4aef7509110888169678fe2161e3af6
- Size of remote file:
- 88.7 kB
- SHA256:
- 2eef91b3724315a2715ebe6ca8880a2ebe7087b92b97a1588f1c90936c392f87
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