Event-triggered reinforcement learning; an application to buildings’ micro-climate control
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article_6.pdf
Description
Published version
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895 KB
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Adobe PDF
Checksum (MD5)
bc111a4f00e10b92415020de84b333e5
Author(s)
Haji Hosseinloo, Ashkan
Dahleh, Munther A
Date Issued
March 2020
Journal
CEUR Workshop Proceedings
Publisher
RWTH Aachen University
Citation
Haji Hosseinloo, Ashkan and Munther Dahleh. et al. “Event-triggered reinforcement learning; an application to buildings’ micro-climate control.” Paper in the CEUR Workshop Proceedings, 2587, AAAI Spring Symposium: MLPS, 2020, virtual meeting, March 23-25 2020, RWTH Aachen University © 2020 The Author(s)
Version
Final published version
Abstract
Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. However, most learning methods for micro-climate control in buildings are based on Markov Decision Processes with fixed transition times that suffer from high variance in the learning phase. Furthermore, ignoring its continuing-task nature the micro-climate control problem is often modeled and solved as an episodic-task problem with discounted rewards. This can result in a wrong optimization solution. To overcome these issues we propose an event-triggered learning control and formulate it based on Semi-Markov Decision Processes with variable transition times and in an average-reward setting. We show via simulation the efficacy of our approach in controlling the micro-climate of a single-zone building.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
http://ceur-ws.org/Vol-2587/article_6.pdf
