Peer Incentive Reinforcement Learning for Cooperative Multiagent Games

doi:10.1109/TG.2022.3196925

	Peer Incentive Reinforcement Learning for Cooperative Multiagent Games
	Zhang, Tianle1,2 ; Liu, Zhen1,2 ; Pu, Zhiqiang1,2 ; Yi, Jianqiang1,2
发表期刊	IEEE TRANSACTIONS ON GAMES
ISSN	2475-1502
	2023-12-01
卷号	15 期号:4 页码:623-636
通讯作者	Liu, Zhen(liuzhen@ia.ac.cn)
摘要	Social learning, especially social incentives, is extremely important for humans to achieve a high level of coordination. Inspired by this, we introduce this concept into cooperative multiagent reinforcement learning (MARL), to implicitly address the credit assignment problem and promote the interagent direct interactions for cooperations among agents in cooperative multiagent games. In this article, we propose a novel intrinsic reward method with peer incentives (IRPI) based on actor-critic policy gradient. This method can enable agents to incentivize each other for their cooperations through using causal influence among them. Specifically, a novel intrinsic reward mechanism is innovatively designed to empower each agent the ability to give positive or negative rewards to other peer agents' actions through considering the causal influence of the other agents on it. The mechanism is realized by a feedforward neural network through utilizing causal influence between the agents. The causal influence of one agent on another is inferred via counterfactual reasoning using the joint action-value function in MARL. The quality of the influence is assessed via counterfactual reasoning using the individual value function in MARL. Simulations are carried out on two popular multiagent game testbeds: Starcraft II Micromanagement and Multiagent Particle Environments. Simulation results demonstrate that the proposed IRPI can enhance cooperations among the agents to achieve better performance compared with a number of state-of-the-art MARL methods in a variety of cooperative multiagent games.
关键词	Cooperative multiagent games intrinsic reward multiagent reinforcement learning (MARL) Starcraft II Micromanagement
DOI	10.1109/TG.2022.3196925
关键词[WOS]	STARCRAFT ; LEVEL
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
项目资助者	National Key Research and Development Program of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Software Engineering
WOS记录号	WOS:001128375200007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54905
专题	复杂系统认知与决策实验室
通讯作者	Liu, Zhen
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhang, Tianle,Liu, Zhen,Pu, Zhiqiang,et al. Peer Incentive Reinforcement Learning for Cooperative Multiagent Games[J]. IEEE TRANSACTIONS ON GAMES,2023,15(4):623-636.
APA	Zhang, Tianle,Liu, Zhen,Pu, Zhiqiang,&Yi, Jianqiang.(2023).Peer Incentive Reinforcement Learning for Cooperative Multiagent Games.IEEE TRANSACTIONS ON GAMES,15(4),623-636.
MLA	Zhang, Tianle,et al."Peer Incentive Reinforcement Learning for Cooperative Multiagent Games".IEEE TRANSACTIONS ON GAMES 15.4(2023):623-636.