A Global Optimal Task Allocation Model for Large-scale Agents Based on Mean Field Game
Sanjin Huang, Wang Yao, Zijia Niu, Xiao Zhang · 2024
Nowadays, task allocation methods for large-scale agents have significant application value across various fields. However, as the scale grows, the interactions between agents will increase exponentially, resulting in a dimensionality explosion problem. Therefore, finding the global optimal solution to the task allocation problem of large-scale agents has become a challenge. Aiming at global social optimal, this paper proposes a task allocation method for large-scale agents based on mean field game (MFG). The game goal of the proposed method is to minimize the total cost of task allocation for all agents and the existence of Nash equilibrium is proved. In the numerical experiments, we compare the proposed method with the task allocation method based on the greedy algorithm and the brute-force search algorithm respectively, and verify the global optimality, effectiveness, and high efficiency of the proposed method.