# Learning Reward Functions by Integrating Human Demonstrations and Preferences

## TL;DR

This paper introduces DemPref, a reward learning framework combining demonstrations and preferences to improve efficiency and accuracy in training autonomous robots, outperforming traditional IRL and preference-based methods.

## Contribution

DemPref integrates demonstrations with preference queries to enhance reward learning, reducing data requirements and improving query quality compared to existing methods.

## Key findings

- DemPref is more efficient than standard preference-based learning.
- Users prefer DemPref over IRL for training robots.
- DemPref achieves better reward function accuracy in experiments.

## Abstract

Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories. In robotics however, IRL often struggles because it is difficult to get high-quality demonstrations; conversely, preference-based learning is very inefficient since it attempts to learn a continuous, high-dimensional function from binary feedback. We propose a new framework for reward learning, DemPref, that uses both demonstrations and preference queries to learn a reward function. Specifically, we (1) use the demonstrations to learn a coarse prior over the space of reward functions, to reduce the effective size of the space from which queries are generated; and (2) use the demonstrations to ground the (active) query generation process, to improve the quality of the generated queries. Our method alleviates the efficiency issues faced by standard preference-based learning methods and does not exclusively depend on (possibly low-quality) demonstrations. In numerical experiments, we find that DemPref is significantly more efficient than a standard active preference-based learning method. In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.