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We interpret solving the multi-vehicle routing problem as a team Markov gamewith partially observable costs. For a given set of customers to serve, theplaying agents (vehicles) have the common goal to determine the team-optimalagent routes with minimal total cost. Each agent thereby observes only its owncost. Our multi-agent reinforcement learning approach, the so-calledmulti-agent Neural Rewriter, builds on the single-agent Neural Rewriter tosolve the problem by iteratively rewriting solutions. Parallel agent actionexecution and partial observability require new rewriting rules for the game.We propose the introduction of a so-called pool in the system which serves as acollection point for unvisited nodes. It enables agents to act simultaneouslyand exchange nodes in a conflict-free manner. We realize limited disclosure ofagent-specific costs by only sharing them during learning. During inference,each agents acts decentrally, solely based on its own cost. First empiricalresults on small problem sizes demonstrate that we reach a performance close tothe employed OR-Tools benchmark which operates in the perfect cost informationsetting.

Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs