Imperfect Information Game in Multiplayer No-limit Texas Hold’em Based on Mean Approximation and Deep CFVnet

Abstract

Imperfect information game in multiplayer no-limit Texas Hold’em is a critical challenge in AI research. Recent advanced solving approaches, such as deep CounterFactual Value networks(CFVnet) combined with continual resolving, provide a way to conduct depth-limited search in imperfect-information games. However, CFVnet has limited deployment in Heads-Up No-Limit Texas Hold’em, and is hard to scale to multiplayer setting. In this paper, we propose a novel algorithm, mean approximation, that effectively converting multi-agent interactions to two-agent interactions, and introduce a useful trick virtual action generation to solve conflicts occur in this conversion. Furthermore, we introduce several improvements to deep CFVnet applied in Texas Hold’em Poker. We combined all above improvements and extensions of CFVnet to create our poker AI MuCFVnet, unlocking the potential of deep CFVnet. Experimental results show that MuCFVnet has strong performance, successfully beats some available public multiplayer poker AI.