A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
Machine Learning
2023-03-07 v2
Abstract
Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck identification in parallel with learning the specifications of the underlying network. Within this framework, we adapt and study various combinatorial semi-bandit methods such as epsilon-greedy, LinUCB, BayesUCB, NeuralUCB, and Thompson Sampling. In addition, our framework is capable of using contextual information in the form of contextual bandits. Finally, we evaluate our framework on the real-world application of road networks and demonstrate its effectiveness in different settings.
Cite
@article{arxiv.2206.08144,
title = {A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification},
author = {Fazeleh Hoseini and Niklas Åkerblom and Morteza Haghir Chehreghani},
journal= {arXiv preprint arXiv:2206.08144},
year = {2023}
}