Data-Driven Cooperative Multi-Task Assignment in Heterogeneous Multi-Agent Pursuit-Evade Game

In this paper, a task assignment method is proposed to deal with the multi-agent pursuit-evade game for heterogeneous unnamed aerial vehicles via reinforcement learning. The mathematical model based on the local position error dynamics is established to describe the interactions among the vehicles in the pursuit-evade game, subject to high nonlinearities and parameter uncertainties involved in the vehicle model. The execution costs and the corresponding optimal control policies of the agent pursuing each target are calculated, and the policy with minimum execution cost is determined as the objective of the multi-agent pursuit-evade game. Min-Max strategy is introduced to estimate and counteract the interaction effects in the mathematical model, and the reinforcement learning-based algorithm is proposed to obtain the optimal solution to the assignment problem based on the Hamilton-Jacobi-Bellman equation without the interaction effects. Simulation results are given to show the effectiveness of the proposed task assignment method.