Q-learning as a monotone scheme
Machine Learning
2024-06-03 v1
Abstract
Stability issues with reinforcement learning methods persist. To better understand some of these stability and convergence issues involving deep reinforcement learning methods, we examine a simple linear quadratic example. We interpret the convergence criterion of exact Q-learning in the sense of a monotone scheme and discuss consequences of function approximation on monotonicity properties.
Cite
@article{arxiv.2405.20538,
title = {Q-learning as a monotone scheme},
author = {Lingyi Yang},
journal= {arXiv preprint arXiv:2405.20538},
year = {2024}
}