8 citations · 9 across the 7 of their papers we have counts for
5 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…
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
Stephane Hatgis-Kessell, Emma Brunskill
We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorith…
Influencing Humans to Conform to Preference Models for RLHF
Stephane Hatgis-Kessell, W. Bradley Knox, Serena Booth +1
Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assuming, implicitly or explicitly, a model o…
Learning Optimal Advantage from Preferences and Mistaking it for Reward
W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson +4
We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF). Most re…
Models of human preference for learning reward functions
W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth +3
The utility of reinforcement learning is limited by the alignment of reward functions with the interests of human stakeholders. One promising method for alignment is to learn the r…