Multi-AGV Scheduling based on Hierarchical Intrinsically Rewarded Multi-Agent Reinforcement Learning

Abstract

Automated Guided Vehicle (AGV) has been widely used in automated warehouses and flexible manufacture systems for material delivery. As a flexible robot, AGV can finish automatic transportation of raw materials in different locations. The proper AGV scheduling strategy can effectively reduce the overall delivery time. To eliminate the large scheduling overhead from the centralized methods, we propose a multi-AGV distributed scheduling scheme in this paper. In particular, we design a Hierarchical Intrinsic Reward Mechanism (HIRM) for the multi-agent reinforcement learning to improve the convergence speed and the final policy level. Based on it, we propose the HIRM Bidirectionally-Coordinated Network (HIRM-BiCNet) based multi-AGV distributed scheduling scheme, to improve the scheduling success rate. The proposed scheme avoids the dependence on the global information and explicit communication. Experiment results demonstrate that our approach achieves impressive results at increase in scheduling success rate (30.75%) and decrease in scheduling time (16 time steps) compared to existing schemes.