Characteristics Verification of the luggage transport problem using relative vectors in multi-agent reinforcement learning

Abstract

In recent years, researchers have developed multi-agent reinforcement learning systems to automate luggage transfer. However, these systems often struggle to learn effectively in partially observable environments, such as POMDPs. This paper presents a novel learning approach that leverages relative vectors to address this limitation. The proposed method is compared to conventional approaches, and the results show that it can achieve better performance in POMDPs.