Mixture of Public and Private Distributions in Imperfect Information Games

Abstract

In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private information can lead to a highly exploitable performance. Yet, excessive attention to it leads to hesitations that are no longer consistent with our private information. In our work, we show that to improve performance, one must choose whether to use a player's private information. We extend our work by proposing a new belief distribution depending on the amount of private and public information desired. We empirically demonstrate an increase in performance and, with the aim of further improving performance, the new distribution should be used according to the position in the game. Our experiments have been done on multiple benchmarks and in multiple determinization-based algorithms (PIMC and IS-MCTS).

arXiv commentAccepted in CoG 2023

References in corpus (6)

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Dota 2 with Large Scale Deep Reinforcement Learning
• DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
• Bayes' Bluff: Opponent Modelling in Poker
• OpenSpiel: A Framework for Reinforcement Learning in Games
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Cited by in corpus (1)

• Perfect Information Monte Carlo with Postponing Reasoning