Multi-Target Tracking Method Based on Multi-Agent Collaborative Reinforcement Learning

Abstract

There are multiple problems with existing multi-target tracking methods,including low learning speed,inefficient tracking and high difficulty in collaborative tracking strategy design.To this end,this paper proposes an improved multi-target tracking method.The method builds a task assignment model based on the number of target agents and tracking agents and their environmental information.Then the model is solved by using Hungary algorithm according to the distance benefit matrix to acquire the task assignment information of multiple tracking agents,which is optimized to shorten the tracking paths of target agents.In addition,the multi-agent collaborative reinforcement learning algorithm is used to enable multiple agents to repeat the process of exploration-accumulation-learning-decision in the same environment and update the strategy based on empirical data to finally complete the multi-target tracking task.Simulation results show that compared with DDPG and MADDPG methods,the proposed method enables multiple agents to collaboratively form the shortest path for tracking multiple moving targets with collisions and obstacles avoided.