IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2234-d1143662.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/10/2234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alhussein Fawzi & Matej Balog & Aja Huang & Thomas Hubert & Bernardino Romera-Paredes & Mohammadamin Barekatain & Alexander Novikov & Francisco J. R. Ruiz & Julian Schrittwieser & Grzegorz Swirszcz & , 2022. "Discovering faster matrix multiplication algorithms with reinforcement learning," Nature, Nature, vol. 610(7930), pages 47-53, October.
    2. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
    5. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    2. Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Jin, Jiahuan & Cui, Tianxiang & Bai, Ruibin & Qu, Rong, 2024. "Container port truck dispatching optimization using Real2Sim based deep reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(1), pages 161-175.
    4. Zhang, Qin & Liu, Yu & Xiang, Yisha & Xiahou, Tangfan, 2024. "Reinforcement learning in reliability and maintenance optimization: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    6. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    7. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.
    8. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    9. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    10. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
    11. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
    12. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
    13. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
    14. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
    15. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 61-87, National Bureau of Economic Research, Inc.
    16. Wang, Xin & Liu, Shuo & Yu, Yifan & Yue, Shengzhi & Liu, Ying & Zhang, Fumin & Lin, Yuanshan, 2023. "Modeling collective motion for fish schooling via multi-agent reinforcement learning," Ecological Modelling, Elsevier, vol. 477(C).
    17. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
    18. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    19. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    20. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2234-:d:1143662. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.