Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use moonlightlane/RL-course-atari-spaceinvader-dpn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use moonlightlane/RL-course-atari-spaceinvader-dpn with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="moonlightlane/RL-course-atari-spaceinvader-dpn", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from moonlightlane/RL-course-atari-spaceinvader-dpn: direct link, hf CLI and curl.
- Browser
- Download file 37.6 kB
-
https://huggingface.co/moonlightlane/RL-course-atari-spaceinvader-dpn/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://moonlightlane/RL-course-atari-spaceinvader-dpn/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/moonlightlane/RL-course-atari-spaceinvader-dpn/resolve/main/train_eval_metrics.zip
37.6 kB
- Xet hash:
- 300882b4a17dde081c4c2443ec35b7203548f0ddf8211b62f45e0f4dd9b6ef28
- Size of remote file:
- 37.6 kB
- SHA256:
- e9026ec59b92e3fef5f5cf0ff393a6158a6a115175aa13d2457c4b0579fc3036
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