Learning Optimal Advantage from Preferences and Mistaking it for Reward.

W. Bradley Knox*

Sigurdur Orn Adalgeirsson*

Serena Booth*

Anca Dragan*

Peter Stone

Scott Niekum*

* External authors

AAAI-24

2024

Abstract

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 recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return. Recent work casts doubt on the validity of this assumption, proposing an alternative preference model based upon regret. We investigate the consequences of assuming preferences are based upon partial return when they actually arise from regret. We argue that the learned function is an approximation of the optimal advantage function,