Empirical Q-Value Iteration
Abstract
We propose a new simple and natural algorithm for learning the optimal Q-value function of a discounted-cost Markov Decision Process (MDP) when the transition kernels are unknown. Unlike the classical learning algorithms for MDPs, such as Q-learning and actor-critic algorithms, this algorithm doesn't depend on a stochastic approximation-based method. We show that our algorithm, which we call the empirical Q-value iteration (EQVI) algorithm, converges to the optimal Q-value function. We also give a rate of convergence or a non-asymptotic sample complexity bound, and also show that an asynchronous (or online) version of the algorithm will also work. Preliminary experimental results suggest a faster rate of convergence to a ball park estimate for our algorithm compared to stochastic approximation-based algorithms.
Cite
@article{arxiv.1412.0180,
title = {Empirical Q-Value Iteration},
author = {Dileep Kalathil and Vivek S. Borkar and Rahul Jain},
journal= {arXiv preprint arXiv:1412.0180},
year = {2019}
}