A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

TL;DR

This paper presents a novel multi-agent reinforcement learning framework that enhances resource balancing in complex logistics networks by fostering cooperation among agents, outperforming traditional optimization methods in simulation.

Contribution

The paper introduces a cooperative multi-agent reinforcement learning approach with innovative state and reward design tailored for complex logistics resource balancing.

Findings

Significant performance improvement over traditional methods

Enhanced cooperation among agents in simulated environments

Greater stability in resource management tasks

Abstract

Resource balancing within complex transportation networks is one of the most important problems in real logistics domain. Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting. However, the high complexity of transportation routes, severe uncertainty of future demand and supply, together with non-convex business constraints make it extremely challenging in the traditional resource management field. In this paper, we propose a novel sophisticated multi-agent reinforcement learning approach to address these challenges. In particular, inspired by the externalities especially the interactions among resource agents, we introduce an innovative cooperative mechanism for state and reward design resulting in more effective and efficient transportation. Extensive experiments on a simulated ocean transportation service demonstrate that our new approach can stimulate cooperation among agents and lead to much better performance. Compared with traditional solutions based on combinatorial optimization, our approach can give rise to a significant improvement in terms of both performance and stability.