Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
atari
space-invaders
Eval Results (legacy)
Instructions to use PotentialRunner/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use PotentialRunner/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="PotentialRunner/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
DQN playing Space Invaders
Trained with Deep Q-Learning (DQN) using stable-baselines3.
Training
- Algorithm: DQN
- Environment: SpaceInvadersNoFrameskip-v4 (Atari)
- Timesteps: 10,000,000
- Environments: 8 parallel
- Batch size: 256
- GPU: Quadro RTX 5000 (16GB)
- Training time: ~5.5 hours
Results
- Final evaluation reward: 1175 +/ 216
- Certification score: 959 (threshold: 200)
Replay
- Downloads last month
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Evaluation results
- mean_reward on Space Invaders (Atari)self-reported1175.00 +/ 215.89