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Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features

Wenzhang Liu Lu Dong Dan Niu Changyin Sun

自动化学报（英文版）2022，Vol.9Issue(9)：1673-1686,14.

自动化学报（英文版）2022，Vol.9Issue(9)：1673-1686,14.DOI:10.1109/JAS.2022.105809

Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features

Wenzhang Liu ¹Lu Dong ²Dan Niu ³Changyin Sun⁴

作者信息

1. School of Artificial Intelligence,Anhui University,Hefei 230039,and also with the Peng Cheng Laboratory,Shenzhen 518055,China
2. School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China
3. School of Automation,Southeast University,Nanjing 210096,China
4. School of Automation,Southeast University,Nanjing 210096
折叠

摘要

关键词

Knowledge transfer/multi-agent systems/reinforce-ment learning/successor features

Key words

Knowledge transfer/multi-agent systems/reinforce-ment learning/successor features

引用本文复制引用

Wenzhang Liu,Lu Dong,Dan Niu,Changyin Sun..Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features[J].自动化学报（英文版）,2022,9(9):1673-1686,14.

基金项目

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 （2021ZD0112700,2018AAA0101400）

自动化学报（英文版）

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