Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use dhingratul/dqn-SpaceInvadersNoFrameskip-v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use dhingratul/dqn-SpaceInvadersNoFrameskip-v with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="dhingratul/dqn-SpaceInvadersNoFrameskip-v", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from dhingratul/dqn-SpaceInvadersNoFrameskip-v: direct link, hf CLI and curl.
- Browser
- Download file 37.5 kB
-
https://huggingface.co/dhingratul/dqn-SpaceInvadersNoFrameskip-v/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://dhingratul/dqn-SpaceInvadersNoFrameskip-v/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/dhingratul/dqn-SpaceInvadersNoFrameskip-v/resolve/main/train_eval_metrics.zip
37.5 kB
- Xet hash:
- 11e37adeef49cb84daffb1c510ab5d216368a52c563997a4d804b99fa8e79d18
- Size of remote file:
- 37.5 kB
- SHA256:
- 8553fb29a83bcc16e551c31f31f521c253c37f2350f0dea30972ae22b67c21eb
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