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arXiv · 2212.07313

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

Tobias Enders·James Harrison·Marco Pavone·Maximilian Schiffer
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Abstract

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.

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BibTeXRIS

Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer. 2022-12-14. Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. https://arxiv.org/abs/2212.07313

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