Bayesian Inference in Monte-Carlo Tree Search

Gerald Tesauro; V T Rajan; Richard Segal

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arXiv · 1203.3519

Bayesian Inference in Monte-Carlo Tree Search

Gerald Tesauro·V T Rajan·Richard Segal

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Abstract

Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. This paper proposes a Bayesian approach to MCTS that is inspired by distributionfree approaches such as UCT [13], yet significantly differs in important respects. The Bayesian framework allows potentially much more accurate (Bayes-optimal) estimation of node values and node uncertainties from a limited number of simulation trials. We further propose propagating inference in the tree via fast analytic Gaussian approximation methods: this can make the overhead of Bayesian inference manageable in domains such as Go, while preserving high accuracy of expected-value estimates. We find substantial empirical outperformance of UCT in an idealized bandit-tree test environment, where we can obtain valuable insights by comparing with known ground truth. Additionally we rigorously prove on-policy and off-policy convergence of the proposed methods.

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BibTeX RIS

Gerald Tesauro, V T Rajan, Richard Segal. 2012-03-15. Bayesian Inference in Monte-Carlo Tree Search. https://arxiv.org/abs/1203.3519

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