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Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning
Regular Papers | Updated：2025-05-06

    • Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning

      Enhanced Publication
    • 完全合作场景中的优化方法：多智能体强化学习综述
    • “Multiagent reinforcement learning (MARL) has emerged as a promising new frontier in reinforcement learning, with significant potential across various applications. This review, introduces its research progress in the field of MARL, meticulously reviews the application of simulation environments in cooperative scenarios, and discusses future trends and potential research directions, laying a foundation for the construction of multiagent systems.”
    • Frontiers of Information Technology & Electronic Engineering   Vol. 26, Issue 4, Pages: 479-509(2025)

    • DOI：10.1631/FITEE.2400259    

      CLC： TP181

    • Received：06 April 2024，

      Revised：06 September 2024，

      Published：2025-04

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  • Tao YANG, Xinhao SHI, Qinghan ZENG, et al. Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning[J]. Frontiers of information technology & electronic engineering, 2025, 26(4): 479-509. DOI： 10.1631/FITEE.2400259.

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