# Learning and Improving Backgammon Strategy

arXiv: https://arxiv.org/abs/2504.02221 · HTML: https://mlsanity.com/paper/2504.02221/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 3 Apr 2025.

## Abstract (arXiv)

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.