Multi-Agent Soft Actor-Critic with Global Loss for Autonomous Mobility-on-Demand Fleet Control

• URL: http://arxiv.org/abs/2404.06975v1
• Date: Wed, 10 Apr 2024 13:49:20 GMT
• Title: Multi-Agent Soft Actor-Critic with Global Loss for Autonomous Mobility-on-Demand Fleet Control
• Authors: Zeno Woywood, Jasper I. Wiltfang, Julius Luy, Tobias Enders, Maximilian Schiffer,
• Abstract summary: We study a sequential decision-making problem for a profit-maximizing operator of an Autonomous Mobility-on-Demand system. We employ a multi-agent Soft Actor-Critic algorithm combined with weighted bipartite matching. 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.
• Score: 1.9503475832401784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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