摘要：Multi-agent reinforcement learning has recently been applied to solve pursuit ***,it suffers from a large number of time steps per training episode,thus always struggling to converge effectively,resulting in low rewards and an inability for agents to learn *** paper proposes a deep reinforcement learning(DRL)training method that employs an ensemble segmented multi-reward function design approach to address the convergence problem mentioned *** ensemble reward function combines the advantages of two reward functions,which enhances the training effect of agents in long ***,we eliminate the non-monotonic behavior in reward function introduced by the trigonometric functions in the traditional 2D polar coordinates observation *** results demonstrate that this method outperforms the traditional single reward function mechanism in the pursuit scenario by enhancing agents’policy scores of the *** ideas offer a solution to the convergence challenges faced by DRL models in long episode pursuit problems,leading to an improved model training performance.