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A MADRL-Based Credit Allocation Approach for Interactive Multi-Agents

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  • Ershen Wang

    (School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, P. R. China†School of Civil Aviation, Shenyang Aerospace University, Shenyang 110136, P. R. China‡The 54th Research Institute of CETC, Shijiazhuang 050001, P. R. China)

  • Xiaotong Wu

    (School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, P. R. China)

  • Chen Hong

    (�Multi-Agent Systems Research Centre, Beijing Union University, Beijing 100101, P. R. China¶The College of Robotics, Beijing Union University, Beijing 100101, P. R. China)

  • Xinna Shang

    (�Multi-Agent Systems Research Centre, Beijing Union University, Beijing 100101, P. R. China¶The College of Robotics, Beijing Union University, Beijing 100101, P. R. China)

  • Peifeng Wu

    (��School of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, P. R. China)

  • Chenglong He

    (��The 54th Research Institute of CETC, Shijiazhuang 050001, P. R. China**The 54th Research Institute of CETC, State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050001, P. R. China)

  • Pingping Qu

    (School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, P. R. China)

Abstract

In multi-agent systems (MAS), the interactions and credit allocation among agents are essential for achieving efficient cooperation. To enhance the interactivity and efficiency of credit allocation in multi-agent reinforcement learning, we introduce a credit allocation for interactive multi-agents method (CAIM). CAIM not only considers the effects of various actions on other agents but also leverages attention mechanisms to handle the mismatch between observations and actions. With a unique credit allocation strategy, agents can more precisely assess their contributions during collaboration. Experiments in various adversarial scenarios within the SMAC benchmark environment indicate that CAIM markedly outperforms existing multi-agent reinforcement learning approaches. Further ablation studies confirm the effectiveness of each CAIM component. This research presents a new paradigm for enhancing collaboration efficiency and overall performance in MAS.

Suggested Citation

  • Ershen Wang & Xiaotong Wu & Chen Hong & Xinna Shang & Peifeng Wu & Chenglong He & Pingping Qu, 2025. "A MADRL-Based Credit Allocation Approach for Interactive Multi-Agents," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 24(07), pages 2117-2137, October.
    • Handle: RePEc:wsi:ijitdm:v:24:y:2025:i:07:n:s0219622025500312
    DOI: 10.1142/S0219622025500312
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