DAFRL: a Dynamic Adaptive Mean Field Game-Based Multi-Agent Cooperative Decision-Making Method

Aiming at the problems of insufficient coordination accuracy and poor adaptability to dynamic environments caused by complex coupling relationships in large-scale heterogeneous multi-agent systems, this paper proposes a cooperative decision-making method that balances rationality and adaptability. Firstly, To address the limitation that uniform weights in traditional mean field games cannot characterize the differentiated contributions of heterogeneous agents, this paper proposes a learnable heterogeneous weight mechanism. The weights are dynamically generated and adaptively updated by the environmental perception network to explicitly reflect the influence of different types of agents on group behavior. On this basis, we further present a heterogeneous weighted mean field game model, which quantifies the differentiated impacts of different types of agents on group behaviors through type-level dynamic weights, breaking through the limitations of uniform weights in traditional mean field theory. Secondly, a dynamic adaptive mean field decision-making framework is designed; an environment perception module is introduced to update the reward function and state transition parameters in real time, and combined with reinforcement learning, the Dynamic Adaptive Mean Field Reinforcement Learning (DAFRL) algorithm is constructed to achieve real-time tracking of equilibrium solutions. Finally, experiments conducted on the scenario of red-blue UAV swarm confrontation demonstrate that the proposed heterogeneous weighted modeling method effectively addresses the coupling problem of heterogeneous agents, and the dynamic adaptive framework significantly enhances environmental robustness. DAFRL exhibits comprehensive advantages in efficiency, accuracy and stability, thereby providing theoretical and technical support for multi-agent cooperative decision-making in complex scenarios.