Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games
文献类型：期刊论文
| 作者 | Zhu, Yuanheng1,2 ; Zhao, Dongbin1,2
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 33期号:3页码:1228-1241 |
| 关键词 | Games Nash equilibrium Mathematical model Markov processes Convergence Dynamic programming Training Deep reinforcement learning (DRL) generalized policy iteration (GPI) Markov game (MG) Nash equilibrium Q network zero sum |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2020.3041469 |
| 通讯作者 | Zhao, Dongbin(dongbin.zhao@ia.ac.cn) |
| 英文摘要 | The Nash equilibrium is an important concept in game theory. It describes the least exploitability of one player from any opponents. We combine game theory, dynamic programming, and recent deep reinforcement learning (DRL) techniques to online learn the Nash equilibrium policy for two-player zero-sum Markov games (TZMGs). The problem is first formulated as a Bellman minimax equation, and generalized policy iteration (GPI) provides a double-loop iterative way to find the equilibrium. Then, neural networks are introduced to approximate Q functions for large-scale problems. An online minimax Q network learning algorithm is proposed to train the network with observations. Experience replay, dueling network, and double Q-learning are applied to improve the learning process. The contributions are twofold: 1) DRL techniques are combined with GPI to find the TZMG Nash equilibrium for the first time and 2) the convergence of the online learning algorithm with a lookup table and experience replay is proven, whose proof is not only useful for TZMGs but also instructive for single-agent Markov decision problems. Experiments on different examples validate the effectiveness of the proposed algorithm on TZMG problems. |
| WOS关键词 | NONLINEAR-SYSTEMS ; GO ; ALGORITHM ; LEVEL |
| 资助项目 | National Key Research and Development Program of China[2018AAA0102404] ; National Key Research and Development Program of China[2018AAA0101005] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000766269100030 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48234]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhao, Dongbin |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Yuanheng,Zhao, Dongbin. Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(3):1228-1241. |
| APA | Zhu, Yuanheng,&Zhao, Dongbin.(2022).Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(3),1228-1241. |
| MLA | Zhu, Yuanheng,et al."Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.3(2022):1228-1241. |
入库方式： OAI收割
来源：自动化研究所
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