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A Survey on Population-Based Deep Reinforcement Learning

Author

Listed:
  • 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
    Ji Hua Laboratory, Foshan 528251, China
    Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai 200433, China)

  • Lihua Zhang

    (Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
    Institute of Meta-Medical, Fudan University, Shanghai 200433, China
    Jilin Provincial Key Laboratory of Intelligence Science and Engineering, Changchun 130013, China)

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.

Suggested Citation

  • Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2234-:d:1143662
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    References listed on IDEAS

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