Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
arXiv:2212.07313
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.
20 pages, 7 figures, extended version of paper accepted at the 5th Learning for Dynamics & Control Conference (L4DC 2023)