Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use umesh251/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use umesh251/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="umesh251/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from umesh251/dqn-SpaceInvadersNoFrameskip-v4: direct link, hf CLI and curl.
- Browser
- Download file 36.1 kB
-
https://huggingface.co/umesh251/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://umesh251/dqn-SpaceInvadersNoFrameskip-v4/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/umesh251/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
36.1 kB
- Xet hash:
- 44d7a4aa1b903054797b47046738bfab6355547816faacb63c830a018b09c394
- Size of remote file:
- 36.1 kB
- SHA256:
- 26f33dc1d51cad5938a86fb6aef6be5d47bf69998845dd5fdae3822cf6661427
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.