Clinical data based optimal STI strategies for HIV: a reinforcement learning approach

Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Proceedings of Benelearn 2006, 11-12 May 2006, Ghent, Belgium (pdf - 172 KB)

Abstract: This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as fitted Q iteration, on numerically generated data.