Reinforcement Learning in Robust Markov Decision Processes
Abstract
An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.
Cite
Text
Lim et al. "Reinforcement Learning in Robust Markov Decision Processes." Neural Information Processing Systems, 2013.Markdown
[Lim et al. "Reinforcement Learning in Robust Markov Decision Processes." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/lim2013neurips-reinforcement/)BibTeX
@inproceedings{lim2013neurips-reinforcement,
title = {{Reinforcement Learning in Robust Markov Decision Processes}},
author = {Lim, Shiau Hong and Xu, Huan and Mannor, Shie},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {701-709},
url = {https://mlanthology.org/neurips/2013/lim2013neurips-reinforcement/}
}