APReL: A Library for Active Preference-based Reward Learning Algorithms (2108.07259v2)

Abstract: Reward learning is a fundamental problem in human-robot interaction to have robots that operate in alignment with what their human user wants. Many preference-based learning algorithms and active querying techniques have been proposed as a solution to this problem. In this paper, we present APReL, a library for active preference-based reward learning algorithms, which enable researchers and practitioners to experiment with the existing techniques and easily develop their own algorithms for various modules of the problem. APReL is available at https://github.com/Stanford-ILIAD/APReL.

• The paper introduces APReL, a modular library that facilitates active preference-based reward learning through innovative query strategies.
• It implements diverse methods such as volume removal, disagreement, and mutual information to optimize query selection and reduce uncertainty.
• The modular design integrates with simulation environments, enhancing human-robot interaction research and enabling rapid prototyping.

Overview of APReL: A Library for Active Preference-based Reward Learning Algorithms

The paper "APReL: A Library for Active Preference-based Reward Learning Algorithms" presents a modular Python library designed to facilitate research and development in the area of active preference-based reward learning (PBRL). The library, APReL, aims to support researchers by providing readily available implementations of various algorithms and query types, enabling streamlined experimentation and algorithm development.

Background and Problem Definition

In human-robot interaction, it is essential for robots to act in alignment with human preferences, making reward learning a central task. The challenge lies in efficiently learning the reward function that represents human preferences through feedback in the form of comparisons or rankings. However, each piece of feedback delivers minimal information, necessitating effective query selection strategies to optimize learning.

This paper introduces a unified framework for addressing these challenges. The authors define the problem using Markov Decision Processes (MDPs) where the aim is to learn a reward function to optimize robot behavior based on user feedback. They also discuss initializing their model with demonstrations to improve efficiency further.

Techniques and Approaches

APReL incorporates several active learning strategies for constructing effective queries, each characterized by different methodologies for optimizing information gain:

1. Volume Removal: Seeks to maximize the expected reduction in uncertainty over the reward function by selecting queries that would lead to a high change in belief distribution.
2. Disagreement: This approach attempts to enhance query informativeness by focusing on maximally differing trajectories, thus facilitating a broader exploration of the reward space.
3. Mutual Information: Targets trajectories that are likely to resolve current uncertainty about the reward function efficiently.
4. Regret Minimization: Aims at identifying optimal trajectories based on reward estimates to optimize learning efficacy.
5. Thompson Sampling: Utilized to minimize regret and facilitate rapid identification of optimal trajectories during the learning process.

Furthermore, APReL facilitates batch query generation methodologies like Greedy, Medoids, Boundary Medoids, Successive Elimination, and Determinantal Point Processes (DPP), each striving to balance informativeness with diversity to enhance time efficiency in query processing.

Implementation and Usage

The APReL library offers a comprehensive suite of modules to accommodate diverse experimental setups:

• Environment and Trajectory Classes: Designed to work with OpenAI Gym environments, they establish the interface for creating simulated learning environments.
• Query Types: A flexible architecture supporting various query modalities, with the ability to integrate additional query forms.
• User Models and Belief Distributions: Modular components supporting different user modeling paradigms and sampling-based Bayesian learning frameworks.
• Query Optimizers: Implemented to actively generate queries based on specified acquisition functions and batch generation techniques.

Implications and Future Prospects

APReL stands out as a significant contribution to the fields of human-robot interaction and robot learning by simplifying the integration and testing of PBRL algorithms within simulational environments. The paper suggests that APReL has a strong potential to evolve into an evolving platform capable of incorporating the latest advancements in the domain.

Its modularity invites community contributions, which could propel APReL towards encompassing a broad range of learning models and non-Bayesian techniques. The implications of such a resource can be profound in applications like autonomous driving, robotic assistance, and personalized robots, where aligning robot behavior closely with human intentions is crucial.

Looking ahead, enhancements in APReL could involve inclusion of more complex user models, including those handling multimodal feedback and non-linear reward functions, to broaden its applicability across diverse and real-world deployment conditions.