NeurIPS 2019poster5 citations
A Family of Robust Stochastic Operators for Reinforcement Learning
Yingdong Lu, Mark Squillante, Chai Wah Wu
Abstract
We consider a new family of stochastic operators for reinforcement learning with the goal of alleviating negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing that our family of operators preserve optimality and increase the action gap in a stochastic sense. Our empirical results illustrate the strong benefits of our robust stochastic operators, significantly outperforming the classical Bellman operator and recently proposed operators.
BibTeX
@inproceedings{NEURIPS2019_a44ba908,
author = {Lu, Yingdong and Squillante, Mark and Wu, Chai Wah},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {A Family of Robust Stochastic Operators for Reinforcement Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/a44ba9086b2b83ccf2baf7c678723449-Paper.pdf},
volume = {32},
year = {2019}
}