Two-timescale Stochastic Approximation (SA) algorithms are widely used in Reinforcement Learning (RL). In such methods, the iterates consist of two parts that are updated using different stepsizes. We develop the first convergence rate result for these algorithms; in particular, we provide a general methodology for analyzing two-timescale linear SA. We apply our methodology to two-timescale RL ...

BACKGROUND The placenta is of utmost importance for intrauterine fetal development and growth. Deregulation of placentation can lead to adverse outcomes for both mother and fetus, e.g. gestational trophoblastic disease (GTD), pre-eclampsia and fetal growth retardation. A significant factor in placental development and function is epigenetic regulation. METHODS This review summarizes the curre...

In this paper, we describe proximal gradient temporal difference learning, which provides a principled way for designing and analyzing true stochastic gradient temporal difference learning algorithms. We show how gradient TD (GTD) reinforcement learning methods can be formally derived, not with respect to their original objective functions as previously attempted, but rather with respect to pri...

Urban and building typologies have a serious impact on the urban climate determine at large magnitude of overheating heat island intensity. The present study aims to analyze various city planning characteristics mitigation island. effect height, street width, aspect ratio, built area orientation, dimensions open spaces distribution ambient surface temperature in is analyzed using Sydney Metropo...

Knowing the solution to Helmholtz equation in a narrow band, with a width of order wave length around a scatterer, we investigate the possibility to construct the wave fronts predicted by the asymptotic methods Geometrical Optics (GO) and Geometrical Theory of Diffraction (GTD). More specifically we show how to build a polynomial approximation of the asymptotic solution on a circle around the s...