Reinforcement Learning
stable-baselines3
TensorBoard
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use KrishBakshi/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use KrishBakshi/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="KrishBakshi/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.
- Downloads last month
- 221
Evaluation results
- mean_reward on SpaceInvadersNoFrameskip-v4self-reported478.50 +/- 129.54