CASIA OpenIR  > 综合信息系统研究中心  > 飞行器智能技术
Attention Enhanced Reinforcement Learning for Multi agent Cooperation
Pu, Zhiqiang1; Wang, Huimu1,2; Liu, Zhen1; Yi, Jianqiang1; Wu, Shiguang1
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2022-02-17
Pages15
Abstract

In this article, a novel method, called attention enhanced reinforcement learning (AERL), is proposed to address issues including complex interaction, limited communication range, and time-varying communication topology for multi agent cooperation. AERL includes a communication enhanced network (CEN), a graph spatiotemporal long short-term memory network (GST-LSTM), and parameters sharing multi-pseudo critic proximal policy optimization (PS-MPC-PPO). Specifically, CEN based on graph attention mechanism is designed to enlarge the agents' communication range and to deal with complex interaction among the agents. GST-LSTM, which replaces the standard fully connected (FC) operator in LSTM with graph attention operator, is designed to capture the temporal dependence while maintaining the spatial structure learned by CEN. PS-MPC-PPO, which extends proximal policy optimization (PPO) in multi agent systems with parameters' sharing to scale to environments with a large number of agents in training, is designed with multi-pseudo critics to mitigate the bias problem in training and accelerate the convergence process. Simulation results for three groups of representative scenarios including formation control, group containment, and predator-prey games demonstrate the effectiveness and robustness of AERL.

KeywordTraining Reinforcement learning Games Scalability Task analysis Standards Optimization Attention mechanism deep reinforcement learning (DRL) graph convolutional networks multi agent systems
DOI10.1109/TNNLS.2022.3146858
WOS KeywordLEVEL ; GAME ; GO
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018AAA0102404] ; National Natural Science Foundation of China[62073323] ; National Natural Science Foundation of China[61806199] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030403] ; External Cooperation Key Project of Chinese Academy Sciences[173211KYSB20200002]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; External Cooperation Key Project of Chinese Academy Sciences
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000761254200001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification强化与进化学习
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47922
Collection综合信息系统研究中心_飞行器智能技术
Corresponding AuthorWang, Huimu
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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
Pu, Zhiqiang,Wang, Huimu,Liu, Zhen,et al. Attention Enhanced Reinforcement Learning for Multi agent Cooperation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:15.
APA Pu, Zhiqiang,Wang, Huimu,Liu, Zhen,Yi, Jianqiang,&Wu, Shiguang.(2022).Attention Enhanced Reinforcement Learning for Multi agent Cooperation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Pu, Zhiqiang,et al."Attention Enhanced Reinforcement Learning for Multi agent Cooperation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):15.
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