Concentration of Contractive Stochastic Approximation and Reinforcement Learning

Abstract

Using a martingale concentration inequality, concentration bounds `from time $n_0$ on' are derived for stochastic approximation algorithms with contractive maps and both martingale difference and Markov noises. These are applied to reinforcement learning algorithms, in particular to asynchronous Q-learning and TD(0).

arXiv comment20 pages, Accepted for publication in Stochastic Systems