VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning
Wei, Qinglai1,2,3; Li, Yugu1; Zhang, Jie1; Wang, Fei-Yue1,3,4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2022-05-18
Pages14
Corresponding AuthorZhang, Jie(jie.zhang@ia.ac.cn)
AbstractAlthough value decomposition networks and the follow on value-based studies factorizes the joint reward function to individual reward functions for a kind of cooperative multiagent reinforcement problem, in which each agent has its local observation and shares a joint reward signal, most of the previous efforts, however, ignored the graphical information between agents. In this article, a new value decomposition with graph attention network (VGN) method is developed to solve the value functions by introducing the dynamical relationships between agents. It is pointed out that the decomposition factor of an agent in our approach can be influenced by the reward signals of all the related agents and two graphical neural network-based algorithms (VGN-Linear and VGN-Nonlinear) are designed to solve the value functions of each agent. It can be proved theoretically that the present methods satisfy the factorizable condition in the centralized training process. The performance of the present methods is evaluated on the StarCraft Multiagent Challenge (SMAC) benchmark. Experiment results show that our method outperforms the state-of-the-art value-based multiagent reinforcement algorithms, especially when the tasks are with very hard level and challenging for existing methods.
KeywordMathematical models Task analysis Games Q-learning Neural networks Behavioral sciences Training Deep learning graph attention networks (GATs) multiagent systems reinforcement learning
DOI10.1109/TNNLS.2022.3172572
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2021YFE0206100] ; National Natural Science Foundation of China[62073321] ; National Defense Basic Scientific Research Program[JCKY2019203C029] ; Science and Technology Development Fund, Macau[0015/2020/AMJ]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; National Defense Basic Scientific Research Program ; Science and Technology Development Fund, Macau
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000798352100001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49411
Collection复杂系统管理与控制国家重点实验室_复杂系统智能机理与平行控制团队
复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorZhang, Jie
Affiliation1.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
3.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
4.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wei, Qinglai,Li, Yugu,Zhang, Jie,et al. VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:14.
APA Wei, Qinglai,Li, Yugu,Zhang, Jie,&Wang, Fei-Yue.(2022).VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA Wei, Qinglai,et al."VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):14.
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