Mixture of Public and Private Distributions in Imperfect Information Games

TL;DR

This paper introduces a new belief distribution for imperfect information games that balances private and public information, leading to improved performance across various benchmarks and algorithms.

Contribution

It proposes a novel belief distribution that dynamically adjusts the use of private information, enhancing game-playing performance.

Findings

Performance improved with the new distribution

Optimal distribution varies by game position

Effective across multiple algorithms and benchmarks

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).