PureTD: Reinforcement Learning for Backgammon Money Games with No Evaluation-time Search

Abstract:We revisit Tesauro's TD-Gammon for backgammon money games in the setting of no evaluation-time search. Both checker play and cube action (use of the doubling cube) are learned from scratch via self-play reinforcement learning (RL), with minimal hand-coded logic and no expert features. In this setting, we demonstrate that pure self-play RL suffices to train models that reach near-state-of-the-art playing strength. Specifically, for cubeful money games, our search-free model evaluates faster and is substantially stronger than the open-source engines GNU Backgammon and Open Sage running a one-move (1-ply) look-ahead search.

Machine Learning (cs.LG)

arXiv:2608.15146 [cs.LG]

(or arXiv:2608.15146v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.15146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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