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Multi-Robot Cooperation Strategy in Game Environment Using Deep Reinforcement Learning
Zhang HD(张宏达)1,2,3; Li DC(李德才)1,2; He YQ(何玉庆)1,2
Department机器人学研究室
Conference Name2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Conference DateDecember 12-15, 2018
Conference PlaceKuala Lumpur, Malaysia
Source PublicationProceedings of the 2018 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlaceNew York
2018
Pages886-891
Indexed ByEI ; CPCI(ISTP)
EI Accession number20191506772212
WOS IDWOS:000468772200142
Contribution Rank1
ISBN978-1-7281-0376-1
AbstractThe multi-robot system combines the characteristics and advantages of each component robot and can break through the constraints of a single robot capability, greatly expanding the application of the robot. However, in the game environment, multi-robot systems face the challenge of intelligent decision-making in high-dimensional complex dynamic environments. The research progress of multi-agent decision-making strategies in the game environment based on deep reinforcement learning provides a solution for solving the problems faced by multi-robot systems. To this end, based on the deep reinforcement learning method, we analyze the multi-agent collaboration strategy in the game environment and propose a learning method that can measure cooperative information between multiple agents. On this basis, we conduct a Nash equilibrium game strategy analysis on the specific multi-agent game problem--the territory defense, use deep Q learning method to learn the defender's joint defense strategy. We conducted simulation experiments and verified the effectiveness of our method. Furthermore, we conducted experiments on the actual multi-robot system platform and demonstrated the feasibility of multi-agent cooperation strategy in practical multi-robot system based on deep reinforcement learning.
Language英语
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23863
Collection机器人学研究室
Corresponding AuthorZhang HD(张宏达)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Zhang HD,Li DC,He YQ. Multi-Robot Cooperation Strategy in Game Environment Using Deep Reinforcement Learning[C]. New York:IEEE,2018:886-891.
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