Hi-CoLA: High Confidence Lower Bound Approximation Based Reinforcement Learning for Flex-Route Transit Operation Control

Abstract:This paper studies safe policy improvement for real-time operation control of flex-route transit (FRT) systems, where robustness is critical and any performance decrease is undesirable. While reinforcement learning (RL) has shown strong empirical success, its adoption remains limited in safety and cost critical applications, such as intelligent transportation, since learned policies lack statistically certified performance guarantees before deployment. In this paper, we propose the High Confidence Lower Bound Approximation (Hi-CoLA) RL framework, which combines importance sampling and concentration inequalities with a differentiable surrogate network to efficiently estimate the high-confidence performance lower bound and optimize it via policy gradient for robust performance improvement. On the FRT operation control problem, Hi-CoLA outperforms state-of-the-art offline and safe RL methods in both average and tail-risk performance. A case study on an actual route in Chicago, implemented in the SUMO simulator, serves as a proof of concept. Hi-CoLA enhances real-time FRT control with guaranteed performance and is broadly applicable to other safety critical decision-making scenarios.

Robotics (cs.RO)

arXiv:2611.01071 [cs.RO]

(or arXiv:2611.01071v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.01071 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.