  We interpret solving the multi-vehicle routing problem as a team Markov game
with partially observable costs. For a given set of customers to serve, the
playing agents (vehicles) have the common goal to determine the team-optimal
agent routes with minimal total cost. Each agent thereby observes only its own
cost. Our multi-agent reinforcement learning approach, the so-called
multi-agent Neural Rewriter, builds on the single-agent Neural Rewriter to
solve the problem by iteratively rewriting solutions. Parallel agent action
execution 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 a
collection point for unvisited nodes. It enables agents to act simultaneously
and exchange nodes in a conflict-free manner. We realize limited disclosure of
agent-specific costs by only sharing them during learning. During inference,
each agents acts decentrally, solely based on its own cost. First empirical
results on small problem sizes demonstrate that we reach a performance close to
the employed OR-Tools benchmark which operates in the perfect cost information
setting.
