When you "measure data", you quantify its characteristics to support dataset comparison & curation.
You also begin to know what systems will learn. Many ML systems don't reason with this, we posit you should.
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
Feb 8, 2022
Thomas Krendl Gilbert, Sarah Dean, Tom Zick, Nathan Lambert
Center for Long-Term Cybersecurity White paper Series
We detail why reinforcement learning systems pose a different type of (dynamic) risks to society. This paper outlines the different types of feedback present in RL systems, the risks they pose, and a path forward for policymakers.
In this paper we set out to understand the causes of compounding prediction errors in one-step learned models. With this, we hope a next generation of models can be used to improve model-based reinforcement learning.
2020 Conference on Learning for Decision and Control
Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning -- control and dynamics. When optimizing model accuracy, there is no guarantee on improving task performance!