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

Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)1284-1296
Number of pages13
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event5th Annual Conference on Learning for Dynamics and Control, L4DC 2023 - Philadelphia, United States
Duration: 15 Jun 202316 Jun 2023


  • autonomous mobility on demand
  • deep reinforcement learning
  • hybrid learning and optimization
  • multi-agent learning


Dive into the research topics of 'Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems'. Together they form a unique fingerprint.

Cite this