18 citations · 34 across the 10 of their papers we have counts for
3 papers · 1 filter
Specifying Reward Functions for RL Without Environment Sampling
Stephane Hatgis-Kessell, W. Bradley Knox, Emma Brunskill
Enabling human stakeholders to specify reward functions that lead to their desired outcomes is a key challenge in deploying reinforcement learning agents. Preference-based methods…
A Framework for Designing Reward Functions: From Objectives to Features to Human-Aligned Reward Functions
Di Yang Shi, W. Bradley Knox
We present a formal process to enable non-experts to instantiate and iterate on human-aligned reward functions, i.e. reward functions that adhere to a given preference ordering ove…
GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes
Saman Khamesian, Sri Harini Balaji, Di Yang Shi +5
Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target range. Although automated insulin…