Task Assignment in Multi-Agent Games via Reinforcement Learning and Expert Knowledge
Shangheng Li, Hao Liu, Ziming Ren, Dawei Liu, Lan Wei, X.-J. Wang, Mutian Guo · 2024
This paper investigates the task assignment problem in multi-agent games subject to high model nonlinearities and external disturbances on the agents. An optimal task assignment value function is constructed by considering the optimal control of single-agent trajectory tracking and the multi-agent target allocation. The task assignment problem is transformed into an optimal control problem based on the Hamilton-Jacobi-Bellman equations. An integral reinforcement learning method is constructed to solve the Hamilton-JacobiBellman equations online using the input and state data of the multi-agent system. The optimal control policy and total cost of execution are solved without the knowledge of agent model via reinforcement learning and expert knowledge. The simulation results verify the effectiveness of the proposed task assignment algorithm.