We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stochastic digraphs. The effectiveness of the proposed technique is demonstrated by application to the diffusion of epidemic diseases over time-varying contact networks generated by the proximity patterns of mobile agents.
@article{arxiv.2202.12546,
title = {Reachability analysis in stochastic directed graphs by reinforcement learning},
author = {Corrado Possieri and Mattia Frasca and Alessandro Rizzo},
journal= {arXiv preprint arXiv:2202.12546},
year = {2022}
}