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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Scalable Inverse Reinforcement Learning Through Multifidelity Bayesian Optimization.

Mahdi Imani, Seyede Fatemeh Ghoreishi

    IEEE Transactions on Neural Networks and Learning Systems
    |January 22, 2021
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    Summary

    This study introduces a multifidelity Bayesian optimization (MFBO) framework to improve inverse reinforcement learning (IRL) scalability and reliability. The MFBO framework enhances expert policy quantification in complex systems like genomics.

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    Area of Science:

    • Computational Biology
    • Machine Learning
    • Control Theory

    Background:

    • Expert decision-making and policies generate data in various fields, including genomics, metagenomics, and cyber-physical systems.
    • Quantifying these expert policies via data is termed reward function learning, a key aspect of inverse reinforcement learning (IRL).
    • Existing IRL methods often struggle with scalability for large systems and reliability in accurately learning reward functions.

    Purpose of the Study:

    • To propose a novel multifidelity Bayesian optimization (MFBO) framework to enhance the scalability and reliability of existing IRL techniques.
    • To address the limitations of current IRL methods in handling large-scale problems and ensuring accurate reward function learning.

    Main Methods:

    • Introduced a multifidelity Bayesian optimization (MFBO) framework designed to scale inverse reinforcement learning (IRL) processes.
    • The framework integrates multiple approximators, balancing exploration and exploitation by considering their uncertainty and computational costs.
    • MFBO framework is designed to improve the learning process of a wide range of existing IRL techniques.

    Main Results:

    • Demonstrated significant scaling of the IRL learning process through the proposed MFBO framework.
    • The framework effectively incorporates multiple approximators, managing their uncertainties and computational expenses.
    • High performance of the MFBO framework was validated across genomics, metagenomics, and simulated datasets.

    Conclusions:

    • The multifidelity Bayesian optimization (MFBO) framework offers a scalable and reliable solution for reward function learning in inverse reinforcement learning (IRL).
    • This approach enhances the practical applicability of IRL in domains requiring expert policy quantification from data.
    • MFBO shows promise for advancing research in computational biology, cyber-physical systems, and other data-driven decision-making fields.