Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning

TL;DR

This paper introduces SARA, a contrastive framework for preference-based reinforcement learning that is robust to label noise and adaptable to various feedback formats and training settings, improving alignment with human intent.

Contribution

SARA is a novel contrastive approach that learns latent representations to compute rewards, enhancing robustness and versatility in preference-based RL.

Findings

Outperforms baselines on continuous control offline RL benchmarks.

Demonstrates versatility in trajectory filtering, cross-task transfer, and reward shaping.

Resilient to noisy labels and adaptable to diverse feedback formats.

Abstract

Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most previous PbRL work has not investigated the robustness to labeler errors, inevitable with labelers who are non-experts or operate under time constraints. Additionally, PbRL algorithms often target very specific settings (e.g. pairwise ranked preferences or purely offline learning). We introduce Similarity as Reward Alignment (SARA), a simple contrastive framework that is both resilient to noisy labels and adaptable to diverse feedback formats and training paradigms. SARA learns a latent representation of preferred samples and computes rewards as similarities to the learned latent. We demonstrate strong performance compared to baselines on continuous control offline RL benchmarks. We further demonstrate SARA's versatility in applications such as trajectory filtering for downstream tasks, cross-task preference transfer, and reward shaping in online learning.