Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use HuggingDLRL/My_first_DQN_SpaceInvaders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use HuggingDLRL/My_first_DQN_SpaceInvaders with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="HuggingDLRL/My_first_DQN_SpaceInvaders", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from HuggingDLRL/My_first_DQN_SpaceInvaders: direct link, hf CLI and curl.
- Browser
- Download file 36.2 kB
-
https://huggingface.co/HuggingDLRL/My_first_DQN_SpaceInvaders/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://HuggingDLRL/My_first_DQN_SpaceInvaders/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/HuggingDLRL/My_first_DQN_SpaceInvaders/resolve/main/train_eval_metrics.zip
36.2 kB
- Xet hash:
- 5aae9b052df6c0db4f2863def97edf8cb363939c353530ff6271d5bd535f7d90
- Size of remote file:
- 36.2 kB
- SHA256:
- 73720707a4560a75320f71d88844dd2ad06f05cbd45069e32097a4ea4813b48b
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