Non-ergodicity in reinforcement learning: robustness via ergodicity transformations

arXiv:2310.11335v2 Announce Type: replace
Abstract: Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the real world. A significant challenge hindering the adoption of RL methods in these domains is the non-robustness of conventional algorithms. In this paper, we argue that a fundamental issue contributing to this lack of robustness lies in the focus on the expected value of the return as the sole ``correct'' optimization …

abstract adoption agents agriculture algorithms application arxiv autonomous autonomous driving challenge cs.lg decisions domains driving finance paper precision reinforcement reinforcement learning robustness type via world