26 citations · 28 across the 3 of their papers we have counts for
3 papers
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization
Maxim Kaledin, Alexander Golubev, Denis Belomestny
Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimat…
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Maxim Kaledin, Eric Moulines, Alexey Naumov +2
Linear two-timescale stochastic approximation (SA) scheme is an important class of algorithms which has become popular in reinforcement learning (RL), particularly for the policy e…
Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm
D. Belomestny, M. Kaledin, J. Schoenmakers
In this article we propose a Weighted Stochastic Mesh (WSM) Algorithm for approximating the value of a discrete and continuous time optimal stopping problem. We prove that in the d…