83 citations · 84 across the 3 of their papers we have counts for
3 papers
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
L. L. Ankile, B. S. Ham, K. Mao +4
When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design. W…
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Omer Gottesman, Fredrik Johansson, Joshua Meier +16
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL)…
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes
Taylor Killian, Samuel Daulton, George Konidaris +1
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings…