# Active Reward Learning from Online Preferences

## TL;DR

This paper introduces an online active reward learning method that efficiently gathers human preferences through simple pairwise queries, enabling robots to adapt quickly with minimal human effort.

## Contribution

It proposes a novel online query strategy that maximizes information gain and reduces human burden, outperforming baseline methods in various experimental settings.

## Key findings

- Outperforms baseline techniques in simulations and real robot experiments.
- Requires fewer human queries compared to existing methods.
- Enables rapid robot policy adaptation with minimal human input.

## Abstract

Robot policies need to adapt to human preferences and/or new environments. Human experts may have the domain knowledge required to help robots achieve this adaptation. However, existing works often require costly offline re-training on human feedback, and those feedback usually need to be frequent and too complex for the humans to reliably provide. To avoid placing undue burden on human experts and allow quick adaptation in critical real-world situations, we propose designing and sparingly presenting easy-to-answer pairwise action preference queries in an online fashion. Our approach designs queries and determines when to present them to maximize the expected value derived from the queries' information. We demonstrate our approach with experiments in simulation, human user studies, and real robot experiments. In these settings, our approach outperforms baseline techniques while presenting fewer queries to human experts. Experiment videos, code and appendices are found at https://sites.google.com/view/onlineactivepreferences.