Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use txia-m/ppo-SpaceInvadersNoFrameskip-v4-try2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use txia-m/ppo-SpaceInvadersNoFrameskip-v4-try2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="txia-m/ppo-SpaceInvadersNoFrameskip-v4-try2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
- Downloads last month
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Evaluation results
- mean_reward on SpaceInvadersNoFrameskip-v4self-reported538.50 +/- 119.35