policy
Minimax-and-Reinforcement-Learning-in-Checkers
Josue2446 · PyTorch
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Overview
Name
Minimax-and-Reinforcement-Learning-in-Checkers
Author
Josue2446
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
This project compares a Minimax agent with alpha–beta pruning to a reinforcement learning agent trained via self-play in the game of checkers. Using a shared board representation, we evaluate search-based versus learning-based approaches, showing the RL agent outperform Minimax after training.
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
3+17 mentioned but not in catalog yetCompatible environments
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Datasets that reference this policy
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