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.

我们研究了自主移动出行系统的利润最大化操作员的顺序决策问题。通过使用多智能体软演员 - 评论家算法结合加权二分图匹配，我们优化了中央操作员的车辆调度策略，提出了一种新的基于车辆的算法体系结构，并调整了评论家的损失函数以恰当地考虑全局行动。此外，我们还扩展了算法以包含重新平衡功能。通过数值实验证明，我们的方法在调度方面的性能比最先进的基准提高了多达 12.9%，在集成重新平衡方面提高了多达 38.9%。