  We study a sequential decision-making problem for a profit-maximizing
operator of an Autonomous Mobility-on-Demand system. Optimizing a central
operator's vehicle-to-request dispatching policy requires efficient and
effective fleet control strategies. To this end, we employ a multi-agent Soft
Actor-Critic algorithm combined with weighted bipartite matching. We propose a
novel vehicle-based algorithm architecture and adapt the critic's loss function
to appropriately consider global actions. Furthermore, we extend our algorithm
to incorporate rebalancing capabilities. Through numerical experiments, we show
that our approach outperforms state-of-the-art benchmarks by up to 12.9% for
dispatching and up to 38.9% with integrated rebalancing.
