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On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios

Author

Listed:
  • Eva Andrés

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

  • Manuel Pegalajar Cuéllar

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

  • Gabriel Navarro

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

Abstract

In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned.

Suggested Citation

  • Eva Andrés & Manuel Pegalajar Cuéllar & Gabriel Navarro, 2022. "On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios," Energies, MDPI, vol. 15(16), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6034-:d:893123
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    References listed on IDEAS

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    1. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    2. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
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    Cited by:

    1. L. G. B. Ruiz & M. C. Pegalajar, 2023. "Advances in Energy Efficiency through Neural-Network-Based Models," Energies, MDPI, vol. 16(5), pages 1-3, February.

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