ALTERNATIVE SELECTION FUNCTIONS FOR INFORMATION SET MONTE CARLO TREE SEARCH

Authors

  • Viliam Lisy Agent Technology Center Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague

DOI:

https://doi.org/10.14311/AP.2014.54.0333

Abstract

We evaluate the performance of various selection methods for the Monte Carlo Tree Search algorithm in two-player zero-sum extensive-form games with imperfect information. We compare the standard Upper Confident Bounds applied to Trees (UCT) along with the less common Exponential Weights for Exploration and Exploitation (Exp3) and novel Regret matching (RM) selection in two distinct imperfect information games: Imperfect Information Goofspiel and Phantom Tic-Tac-Toe. We show that UCT after initial fast convergence towards a Nash equilibrium computes increasingly worse strategies after some point in time. This is not the case with Exp3 and RM, which also show superior performance in head-to-head matches.

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Published

2014-10-31

How to Cite

Lisy, V. (2014). ALTERNATIVE SELECTION FUNCTIONS FOR INFORMATION SET MONTE CARLO TREE SEARCH. Acta Polytechnica, 54(5), 333–340. https://doi.org/10.14311/AP.2014.54.0333

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Section

Articles