Reinforcement Learning
stable-baselines3
PongNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use nsanghi/a2c-Atari-Pong with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use nsanghi/a2c-Atari-Pong with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="nsanghi/a2c-Atari-Pong", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
A2C Agent playing PongNoFrameskip-v4
This is a trained model of a A2C agent playing PongNoFrameskip-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
...
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Evaluation results
- mean_reward on PongNoFrameskip-v4self-reported-20.50 +/- 0.67