David L. Roberts — accepted AI-conference papers

2024

Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract)

AAAI 2024technical

Recently there has been a proliferation of intrinsic motivation (IM) reward shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the…

Cited by 5 Source ↗PDF ↗Scholar ↗

2017

Interactive Learning from Policy-Dependent Human Feedback

James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts +2

ICML 2017poster

This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and…

Cited by 387 Source ↗PDF ↗Scholar ↗