VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning
文献类型：期刊论文
| 作者 | Wei, Qinglai2,3,4 ; Li, Yugu4; Zhang, Jie4; Wang, Fei-Yue1,2,4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2022-05-18 |
| 页码 | 14 |
| 关键词 | Mathematical models Task analysis Games Q-learning Neural networks Behavioral sciences Training Deep learning graph attention networks (GATs) multiagent systems reinforcement learning |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2022.3172572 |
| 通讯作者 | Zhang, Jie(jie.zhang@ia.ac.cn) |
| 英文摘要 | Although 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. |
| 资助项目 | National 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000798352100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49410]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Zhang, Jie |
| 作者单位 | 1.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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