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Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning

Authors :
Daming Shi
Xudong Guo
Yi Liu
Wenhui Fan
Source :
Entropy, Vol 24, Iss 6, p 774 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Poker has been considered a challenging problem in both artificial intelligence and game theory because poker is characterized by imperfect information and uncertainty, which are similar to many realistic problems like auctioning, pricing, cyber security, and operations. However, it is not clear that playing an equilibrium policy in multi-player games would be wise so far, and it is infeasible to theoretically validate whether a policy is optimal. Therefore, designing an effective optimal policy learning method has more realistic significance. This paper proposes an optimal policy learning method for multi-player poker games based on Actor-Critic reinforcement learning. Firstly, this paper builds the Actor network to make decisions with imperfect information and the Critic network to evaluate policies with perfect information. Secondly, this paper proposes a novel multi-player poker policy update method: asynchronous policy update algorithm (APU) and dual-network asynchronous policy update algorithm (Dual-APU) for multi-player multi-policy scenarios and multi-player sharing-policy scenarios, respectively. Finally, this paper takes the most popular six-player Texas hold ’em poker to validate the performance of the proposed optimal policy learning method. The experiments demonstrate the policies learned by the proposed methods perform well and gain steadily compared with the existing approaches. In sum, the policy learning methods of imperfect information games based on Actor-Critic reinforcement learning perform well on poker and can be transferred to other imperfect information games. Such training with perfect information and testing with imperfect information models show an effective and explainable approach to learning an approximately optimal policy.

Details

Language :
English
ISSN :
10994300
Volume :
24
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
edsdoj.206a1825f4c4d27a3b6101fa2f012e4
Document Type :
article
Full Text :
https://doi.org/10.3390/e24060774