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contributor authorDey, Sourav
contributor authorHenze, Gregor P.
date accessioned2024-04-24T22:35:48Z
date available2024-04-24T22:35:48Z
date copyright3/12/2024 12:00:00 AM
date issued2024
identifier issn2642-6641
identifier otherjesbc_5_1_011005.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295506
description abstractThe incorporation of emerging technologies, including solar photovoltaics, electric vehicles, battery energy storage, smart devices, Internet-of-Things devices, and sensors in buildings, desirable control objectives are becoming increasingly complex, calling for advanced controls approaches. Reinforcement learning (RL) is a powerful method for this. RL can adapt and learn from environmental interaction, but it can take a long time to learn and can be unstable initially due to limited environmental knowledge. In our research, we propose an online RL approach for buildings that uses data-driven surrogate models to guide the RL agent during its early training. This helps the controller learn faster and more stably than the traditional direct plug-and-learn online learning approach. In this research, we propose an online approach in buildings with RL where, with the help of data-driven surrogate models, the RL agent is guided during its early exploratory training stage, aiding the controller to learn a near-optimal policy faster and exhibiting more stable training progress than a traditional direct plug-and-learn online learning RL approach. The agents are assisted in their learning and action with information gained from the surrogate models generating multiple artificial trajectories starting from the current state. The research presented an exploration of various surrogate model-assisted training methods and revealed that models focusing on artificial trajectories around rule-based controls yielded the most stable performance. In contrast, models employing random exploration with a one-step look-ahead approach demonstrated superior overall performance.
publisherThe American Society of Mechanical Engineers (ASME)
titleReinforcement Learning Building Control: An Online Approach With Guided Exploration Using Surrogate Models
typeJournal Paper
journal volume5
journal issue1
journal titleASME Journal of Engineering for Sustainable Buildings and Cities
identifier doi10.1115/1.4064842
journal fristpage11005-1
journal lastpage11005-12
page12
treeASME Journal of Engineering for Sustainable Buildings and Cities:;2024:;volume( 005 ):;issue: 001
contenttypeFulltext


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