Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor FeaturesOA
Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features
Wenzhang Liu;Lu Dong;Dan Niu;Changyin Sun
School of Artificial Intelligence,Anhui University,Hefei 230039,and also with the Peng Cheng Laboratory,Shenzhen 518055,ChinaSchool of Cyber Science and Engineering,Southeast University,Nanjing 211189,ChinaSchool of Automation,Southeast University,Nanjing 210096,ChinaSchool of Automation,Southeast University,Nanjing 210096
Knowledge transfermulti-agent systemsreinforce-ment learningsuccessor features
Knowledge transfermulti-agent systemsreinforce-ment learningsuccessor features
《自动化学报（英文版）》 2022 (9)
机器人集群的智能协同控制理论与方法
1673-1686,14
This work was supported in part by the National Key R&D Program of China(2021ZD0112700,2018AAA0101400),the National Natural Science Foundation of China(62173251,61921004,U1713209),and the Natural Science Foundation of Jiangsu Province of China(BK20202006).Recommended by Associate Editor Weinan Gao.(Corresponding
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