Title

Quantum Amplitude Amplification for Reinforcement Learning

Abstract

Reinforcement learning models require a choice rule for assigning probabilities to actions during learning. This chapter reviews work on a new choice rule based on an amplitude amplification algorithm originally developed in quantum computing. The basic theoretical ideas for amplitude amplification are reviewed, as well as four different simulation applications to a task with a predator learning to catch a prey in a grid world, and one application to human learning in a 4 arm bandit choice task. The applications reviewed in this chapter demonstrate that QRL can improve speed and robustness of learning compared to standard choice rules such as epsilon-greedy and softmax choice rules.

Department(s)

Mechanical and Aerospace Engineering

Comments

Chapter 26

International Standard Serial Number (ISSN)

2198-4182; 2198-4190

Document Type

Book - Chapter

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Springer, All rights reserved.

Publication Date

24 Jun 2021

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