@misc{10481/76972,
year = {2022},
month = {8},
url = {https://hdl.handle.net/10481/76972},
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 energyefficiency
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.},
organization = {project QUANERGY	TED2021-129360B-I00},
organization = {Ecological and Digital Transition R&D projects call 2022, Government of Spain},
publisher = {MDPI},
keywords = {Quantum neural networks},
keywords = {Variational quantum circuits},
keywords = {Quantum reinforcement learning},
keywords = {Energy efficiency},
title = {On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios},
doi = {10.3390/en15166034},
author = {Andrés, Eva and Pegalajar Cuéllar, Manuel and Navarro Garulo, Gabriel},
}