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Resource balancing within complex transportation networks is one of the mostimportant problems in real logistics domain. Traditional solutions on theseproblems leverage combinatorial optimization with demand and supplyforecasting. However, the high complexity of transportation routes, severeuncertainty of future demand and supply, together with non-convex businessconstraints make it extremely challenging in the traditional resourcemanagement field. In this paper, we propose a novel sophisticated multi-agentreinforcement learning approach to address these challenges. In particular,inspired by the externalities especially the interactions among resourceagents, we introduce an innovative cooperative mechanism for state and rewarddesign resulting in more effective and efficient transportation. Extensiveexperiments on a simulated ocean transportation service demonstrate that ournew approach can stimulate cooperation among agents and lead to much betterperformance. Compared with traditional solutions based on combinatorialoptimization, our approach can give rise to a significant improvement in termsof both performance and stability.

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