54 citations · 62 across the 7 of their papers we have counts for
3 papers · 1 filter
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi +2
Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response;…
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning
Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma
The aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new…
Learning Reward Functions by Integrating Human Demonstrations and Preferences
Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk +1
Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses…