387 citations · 971 across the 40 of their papers we have counts for
5 papers · 1 filter
An Extensible Interactive Interface for Agent Design
Matthew Rahtz, James Fang, Anca D. Dragan +1
In artificial intelligence, we often specify tasks through a reward function. While this works well in some settings, many tasks are hard to specify this way. In deep reinforcement…
Adversarial Training with Voronoi Constraints
Marc Khoury, Dylan Hadfield-Menell
Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statist…
Conservative Agency via Attainable Utility Preservation
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change…
The Assistive Multi-Armed Bandit
Lawrence Chan, Dylan Hadfield-Menell, Siddhartha Srinivasa +1
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are a…
On the Utility of Model Learning in HRI
Gokul Swamy, Jens Schulz, Rohan Choudhury +2
Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context…