Anderson Acceleration for Reinforcement Learning
Machine Learning
2018-09-26 v1 Machine Learning
Abstract
Anderson acceleration is an old and simple method for accelerating the computation of a fixed point. However, as far as we know and quite surprisingly, it has never been applied to dynamic programming or reinforcement learning. In this paper, we explain briefly what Anderson acceleration is and how it can be applied to value iteration, this being supported by preliminary experiments showing a significant speed up of convergence, that we critically discuss. We also discuss how this idea could be applied more generally to (deep) reinforcement learning.
Cite
@article{arxiv.1809.09501,
title = {Anderson Acceleration for Reinforcement Learning},
author = {Matthieu Geist and Bruno Scherrer},
journal= {arXiv preprint arXiv:1809.09501},
year = {2018}
}
Comments
European Workshop on Reinforcement Learning (EWRL 2018)