A Survey on Population-Based Deep Reinforcement Learning

Weifan Long, Taixian Hou, Xiaoyi Wei, Shichao Yan, Peng Zhai () and Lihua Zhang ()
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Weifan Long: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Taixian Hou: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Xiaoyi Wei: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Shichao Yan: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Peng Zhai: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Lihua Zhang: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China

Mathematics, 2023, vol. 11, issue 10, 1-17

Abstract: Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can generalize to policies of other players or humans. In this survey, we shed light on population-based deep reinforcement learning (PB-DRL) algorithms, their applications, and general frameworks. We introduce several independent subject areas, including naive self-play, fictitious self-play, population-play, evolution-based training methods, and the policy-space response oracle family. These methods provide a variety of approaches to solving multi-agent problems and are useful in designing robust multi-agent reinforcement learning algorithms that can handle complex real-life situations. Finally, we discuss challenges and hot topics in PB-DRL algorithms. We hope that this brief survey can provide guidance and insights for researchers interested in PB-DRL algorithms.

Keywords: reinforcement learning; multi-agent reinforcement learning; self play; population play (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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