Markov Chain Concentration with an Application in Reinforcement Learning
Machine Learning
2023-01-10 v1 Artificial Intelligence
Abstract
Given random variables whose joint distribution is given as we will use the Martingale Method to show any Lipshitz Function over these random variables is subgaussian. The Variance parameter however can have a simple expression under certain conditions. For example under the assumption that the random variables follow a Markov Chain and that the function is Lipschitz under a Weighted Hamming Metric. We shall conclude with certain well known techniques from concentration of suprema of random processes with applications in Reinforcement Learning
Cite
@article{arxiv.2301.02926,
title = {Markov Chain Concentration with an Application in Reinforcement Learning},
author = {Debangshu Banerjee},
journal= {arXiv preprint arXiv:2301.02926},
year = {2023}
}