Knowledge Commons of Institute of Automation,CAS
UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios | |
Chai, Jiajun1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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ISSN | 2162-237X |
2021-08-27 | |
页码 | 12 |
摘要 | Multiagent reinforcement learning methods, such as VDN, QMIX, and QTRAN, that adopt centralized training with decentralized execution (CTDE) framework have shown promising results in cooperation and competition. However, in some multiagent scenarios, the number of agents and the size of the action set actually vary over time. We call these unshaped scenarios, and the methods mentioned above fail in performing satisfyingly. In this article, we propose a new method, called Unshaped Networks for Multiagent Systems (UNMAS), which adapts to the number and size changes in multiagent systems. We propose the self-weighting mixing network to factorize the joint action-value. Its adaption to the change in agent number is attributed to the nonlinear mapping from each-agent Q value to the joint action-value with individual weights. Besides, in order to address the change in an action set, each agent constructs an individual action-value network that is composed of two streams to evaluate the constant environment-oriented subset and the varying unit-oriented subset. We evaluate UNMAS on various StarCraft II micromanagement scenarios and compare the results with several state-of-the-art MARL algorithms. The superiority of UNMAS is demonstrated by its highest winning rates especially on the most difficult scenario 3s5z_vs_3s6z. The agents learn to perform effectively cooperative behaviors, while other MARL algorithms fail. Animated demonstrations and source code are provided in https://sites.google.com/view/unmas. |
关键词 | Multi-agent systems Training Task analysis Reinforcement learning Sun Learning systems Semantics Centralized training with decentralized execution (CTDE) multiagent reinforcement learning StarCraft II |
DOI | 10.1109/TNNLS.2021.3105869 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018AAA0102404] ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDA27030400] ; Youth Innovation Promotion Association of CAS |
项目资助者 | National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS) ; Youth Innovation Promotion Association of CAS |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000733450200001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 多智能体决策 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46990 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhao, Dongbin |
作者单位 | 1.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.Second Acad CASIS, X Lab, Beijing 100854, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chai, Jiajun,Li, Weifan,Zhu, Yuanheng,et al. UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12. |
APA | Chai, Jiajun.,Li, Weifan.,Zhu, Yuanheng.,Zhao, Dongbin.,Ma, Zhe.,...&Ding, Jishiyu.(2021).UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12. |
MLA | Chai, Jiajun,et al."UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):12. |
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