Learning-Based Adaptive Dynamic Routing with Stability Guarantee for a Single-Origin-Single-Destination Network
Abstract
We consider learning-based adaptive dynamic routing for a single-origin-single-destination queuing network with stability guarantees. Specifically, we study a class of generalized shortest path policies that can be parameterized by only two constants via a piecewise-linear function. Using the Foster-Lyapunov stability theory, we develop a criterion on the parameters to ensure mean boundedness of the traffic state. Then, we develop a policy iteration algorithm that learns the parameters from realized sample paths. Importantly, the piecewise-linear function is both integrated into the Lyapunov function for stability analysis and used as a proxy of the value function for policy iteration; hence, stability is inherently ensured for the learned policy. Finally, we demonstrate via a numerical example that the proposed algorithm learns a near-optimal routing policy with an acceptable optimality gap but significantly higher computational efficiency compared with a standard neural network-based algorithm.
Cite
@article{arxiv.2408.14758,
title = {Learning-Based Adaptive Dynamic Routing with Stability Guarantee for a Single-Origin-Single-Destination Network},
author = {Yidan Wu and Feixiang Shu and Jianan Zhang and Li Jin},
journal= {arXiv preprint arXiv:2408.14758},
year = {2024}
}