Learning And Improving Backgammon Strategy

# Learning and Improving Backgammon Strategy

**arXiv ID:** 2504.02221
**Authors:** Gregory R. Galperin
**Published:** 2025-04-03T02:27:22Z
**Abstract:**
A novel approach to learning is presented, combining features of on-line and off-line methods to achieve considerable performance in the task of learning a backgammon value function in a process that exploits the processing power of parallel supercomputers. The off-line methods comprise a set of techniques for parallelizing neural network training and $TD(λ)$ reinforcement learning; here Monte-Carlo ``Rollouts'' are introduced as a massively parallel on-line policy improvement technique which applies resources to the decision points encountered during the search of the game tree to further augment the learned value function estimate. A level of play roughly as good as, or possibly better than, the current champion human and computer backgammon players has been achieved in a short period of learning.

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## References

- [arXiv:2504.02221](http://arxiv.org/abs/2504.02221v1)