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Learning of winning strategies for terminal games with linear-size memory
Authors:Thomas Böhme  Frank Göring  Zsolt Tuza  Herwig Unger
Institution:1.Institut für Mathematik,Technische Universit?t Ilmenau,Ilmenau,Germany;2.Fakult?t für Mathematik,Technische Universit?t Chemnitz,Chemnitz,Germany;3.Computer and Automation Institute,Hungarian Academy of Sciences,Budapest,Hungary;4.Department of Computer Science,University of Pannonia,Veszprém,Hungary;5.Fachbereich Informatik,Universit?t Rostock,Rostock,Germany
Abstract:We prove that if one or more players in a locally finite positional game have winning strategies, then they can find it by themselves, not losing more than a bounded number of plays and not using more than a linear-size memory, independently of the strategies applied by the other players. We design two algorithms for learning how to win. One of them can also be modified to determine a strategy that achieves a draw, provided that no winning strategy exists for the player in question but with properly chosen moves a draw can be ensured from the starting position. If a drawing- or winning strategy exists, then it is learnt after no more than a linear number of plays lost (linear in the number of edges of the game graph). Z. Tuza’s research has been supported in part by the grant OTKA T-049613.
Keywords:Positional game  Reinforcement learning  Winning strategy  Achieving a draw
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