Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Kibalama/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Kibalama/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Kibalama/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from Kibalama/dqn-SpaceInvadersNoFrameskip-v4: direct link, hf CLI and curl.
- Browser
- Download file 35.1 kB
-
https://huggingface.co/Kibalama/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://Kibalama/dqn-SpaceInvadersNoFrameskip-v4/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/Kibalama/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
35.1 kB
- Xet hash:
- 2af82b0c2f65a826dd51aeeff39558a1547c66415ae3e3a80c9bf8fbd5581049
- Size of remote file:
- 35.1 kB
- SHA256:
- 04341a6390c9eaa34c3e05f5c2dc5662f6131896b40aa459c0708d550ceb1466
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