Multi-Agent Multi-Target Pursuit with Dynamic Target Allocation and Actor Network Optimization

Abstract

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-agent systems using multi-agent deep reinforcement learning algorithms. Multi-agent multi-target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi-target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi-target allocation strategies from different perspectives. We compare our allocation strategies in the multi-agent multi-target pursuit environment that models collision risk and verify the superiority of the allocation strategy marked as POLICY3, considering the overall perspective of agents and targets. We also find that there is a significant gap in the tracking policies learned by agents when using the multi-agent reinforcement learning algorithm MATD3. We propose an improved algorithm, DAO-MATD3, based on dynamic actor network optimization. The simulation results show that the proposed POLICY3-DAO-MATD3 method effectively improves the efficiency of completing multi-agent multi-target pursuit tasks.