Knowledge Commons of Institute of Automation，CAS
| A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents | |
Zhang, Zhen1; Wang, Dongqing1; Zhao, Dongbin2,3 ; Han, Qiaoni1; Song, Tingting1,4
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| 发表期刊 | IEEE ACCESS
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| ISSN | 2169-3536 |
| 2018 | |
| 卷号 | 6页码:70223-70235 |
| 通讯作者 | Zhang, Zhen(tbsunshine8@163.com) |
| 摘要 | Multi-agent reinforcement learning (MARL) can be used to design intelligent agents for solving cooperative tasks. Within the MARL category, this paper proposes the probability of maximal reward based on the infinitesimal gradient ascent (PMR-IGA) algorithm to reach the maximal total reward in repeated games. Theoretical analyses show that in a finite-player-finite-action repeated game with two pure optimal joint actions where no common component action exists, both the optimal joint actions are stable critical points of the PMR-IGA model. Furthermore, we apply the Q-value function to estimate the gradient and derive the probability of maximal reward based on estimated gradient ascent (PMR-EGA) algorithm. Theoretical analyses and simulations of case studies of repeated games show that the maximal total reward can be achieved under any initial conditions. The PMR-EGA can be naturally extended to optimize cooperative stochastic games. Two stochastic games, i.e., box pushing and a distributed sensor network, are used as test beds. The simulations show that the PMR-EGA displays consistently an excellent performance for both stochastic games. |
| 关键词 | Multi-agent reinforcement learning gradient ascent Q-learning cooperative tasks |
| DOI | 10.1109/ACCESS.2018.2878853 |
| 关键词[WOS] | EVOLUTIONARY GAME-THEORY ; POLICY GRADIENT ; SYSTEMS |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | Shandong Provincial Natural Science Foundation of China[ZR2017PF005] ; National Natural Science Foundation of China[61873138] ; National Natural Science Foundation of China[61803218] ; National Natural Science Foundation of China[61573353] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61573205] ; Shandong Provincial Natural Science Foundation of China[ZR2017PF005] ; National Natural Science Foundation of China[61873138] ; National Natural Science Foundation of China[61803218] ; National Natural Science Foundation of China[61573353] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61573205] |
| 项目资助者 | Shandong Provincial Natural Science Foundation of China ; National Natural Science Foundation of China |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:000453261200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/25666 |
| 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 | Zhang, Zhen |
| 作者单位 | 1.Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Qingdao Metro Grp Co Ltd, Operating Branch, Qingdao 266000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zhen,Wang, Dongqing,Zhao, Dongbin,et al. A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents[J]. IEEE ACCESS,2018,6:70223-70235. |
| APA | Zhang, Zhen,Wang, Dongqing,Zhao, Dongbin,Han, Qiaoni,&Song, Tingting.(2018).A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents.IEEE ACCESS,6,70223-70235. |
| MLA | Zhang, Zhen,et al."A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents".IEEE ACCESS 6(2018):70223-70235. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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