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Attention enhanced reinforcement learning for multi-agent cooperation | |
Zhiqiang Pu1![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Neural Networks and Learning Systems
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2022 | |
期号 | 2022页码:1-15 |
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. |
关键词 | Attention mechanism deep reinforcement learning (DRL) graph convolutional networks multi agent systems |
DOI | 10.1109/TNNLS.2022.3146858 |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
七大方向——子方向分类 | 强化与进化学习 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47425 |
专题 | 复杂系统认知与决策实验室_飞行器智能技术 |
通讯作者 | Huimu Wang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhiqiang Pu,Huimu Wang,Zhen Liu,et al. Attention enhanced reinforcement learning for multi-agent cooperation[J]. IEEE Transactions on Neural Networks and Learning Systems,2022(2022):1-15. |
APA | Zhiqiang Pu,Huimu Wang,Zhen Liu,Jianqiang Yi,&Shiguang Wu.(2022).Attention enhanced reinforcement learning for multi-agent cooperation.IEEE Transactions on Neural Networks and Learning Systems(2022),1-15. |
MLA | Zhiqiang Pu,et al."Attention enhanced reinforcement learning for multi-agent cooperation".IEEE Transactions on Neural Networks and Learning Systems .2022(2022):1-15. |
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