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Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning
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 tha...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222241/ https://www.ncbi.nlm.nih.gov/pubmed/35741495 http://dx.doi.org/10.3390/e24060774 |
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author | Shi, Daming Guo, Xudong Liu, Yi Fan, Wenhui |
author_facet | Shi, Daming Guo, Xudong Liu, Yi Fan, Wenhui |
author_sort | Shi, Daming |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9222241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92222412022-06-24 Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning Shi, Daming Guo, Xudong Liu, Yi Fan, Wenhui Entropy (Basel) Article 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. MDPI 2022-05-30 /pmc/articles/PMC9222241/ /pubmed/35741495 http://dx.doi.org/10.3390/e24060774 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shi, Daming Guo, Xudong Liu, Yi Fan, Wenhui Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title | Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title_full | Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title_fullStr | Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title_full_unstemmed | Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title_short | Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning |
title_sort | optimal policy of multiplayer poker via actor-critic reinforcement learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222241/ https://www.ncbi.nlm.nih.gov/pubmed/35741495 http://dx.doi.org/10.3390/e24060774 |
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