A MADRL-Based Credit Allocation Approach for Interactive Multi-Agents
Ershen Wang, Xiaotong Wu, Chen Hong, Xinna Shang, Peifeng Wu, Chenglong He, Pingping Qu · International Journal of Information Technology & Decision Making · 2025
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.