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Attention Enhanced Reinforcement Learning for Multi agent Cooperation

文献类型：期刊论文

作者Pu, Zhiqiang2; Wang, Huimu1,2; Liu, Zhen2; Yi, Jianqiang2; Wu, Shiguang2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-02-17
页码15
ISSN号2162-237X
关键词Training 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
通讯作者Wang, Huimu(wanghuimu2018@ia.ac.cn)
英文摘要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.
WOS关键词LEVEL ; GAME ; GO
资助项目National 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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000761254200001
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/47921]  
专题综合信息系统研究中心_飞行器智能技术
通讯作者Wang, Huimu
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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