  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.
