CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning

TL;DR

CRED introduces a novel environment design and counterfactual reasoning approach to active preference learning, significantly improving reward inference accuracy and efficiency in complex robotic tasks.

Contribution

It proposes a new trajectory generation method that jointly optimizes environment design and trajectory selection using counterfactual reasoning for better preference elicitation.

Findings

Outperforms state-of-the-art in reward accuracy

Achieves higher sample efficiency

Receives higher user ratings

Abstract

As a robot's operational environment and tasks to perform within it grow in complexity, the explicit specification and balancing of optimization objectives to achieve a preferred behavior profile moves increasingly farther out of reach. These systems benefit strongly by being able to align their behavior to reflect human preferences and respond to corrections, but manually encoding this feedback is infeasible. Active preference learning (APL) learns human reward functions by presenting trajectories for ranking. However, existing methods sample from fixed trajectory sets or replay buffers that limit query diversity and often fail to identify informative comparisons. We propose CRED, a novel trajectory generation method for APL that improves reward inference by jointly optimizing environment design and trajectory selection to efficiently query and extract preferences from users. CRED "imagines" new scenarios through environment design and leverages counterfactual reasoning -- by sampling possible rewards from its current belief and asking "What if this were the true preference?" -- to generate trajectory pairs that expose differences between competing reward functions. Comprehensive experiments and a user study show that CRED significantly outperforms state-of-the-art methods in reward accuracy and sample efficiency and receives higher user ratings.