Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective

TL;DR

This paper introduces a novel cooperative multi-agent deep reinforcement learning approach to simultaneously solve target assignment and path planning in intelligent warehouses, considering physical agent dynamics for improved efficiency and effectiveness.

Contribution

It is the first to model and address TAPF in warehouses using cooperative multi-agent deep RL, integrating physical dynamics for the first time.

Findings

Performs well across various task settings.

Achieves near-shortest path planning.

More time-efficient than baseline methods.

Abstract

Multi-agent target assignment and path planning (TAPF) are two key problems in intelligent warehouse. However, most literature only addresses one of these two problems separately. In this study, we propose a method to simultaneously solve target assignment and path planning from a perspective of cooperative multi-agent deep reinforcement learning (RL). To the best of our knowledge, this is the first work to model the TAPF problem for intelligent warehouse to cooperative multi-agent deep RL, and the first to simultaneously address TAPF based on multi-agent deep RL. Furthermore, previous literature rarely considers the physical dynamics of agents. In this study, the physical dynamics of the agents is considered. Experimental results show that our method performs well in various task settings, which means that the target assignment is solved reasonably well and the planned path is almost shortest. Moreover, our method is more time-efficient than baselines.