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

• URL: http://arxiv.org/abs/2212.07313v2
• Date: Wed, 10 May 2023 16:20:51 GMT
• Title: Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
• Authors: Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer
• Abstract summary: 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.
• Score: 31.23491481430466
• License: http://creativecommons.org/licenses/by/4.0/
• 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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