Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact

Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact

TitleDeep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact
Publication TypeConference Paper
Year of Publication2020
AuthorsAnand Prakash, Samir Touzani, Mariam Kiran, Shreya Agarwal, Marco Pritoni, Jessica Granderson
Conference NameRLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
Date Published11/17/2020
Keywordscontrol systems, Distributed energy resources, reinforcement learning, Smart buildings
Abstract

As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.

URLhttps://dl.acm.org/doi/10.1145/3427773.3427868
DOI10.1145/3427773.3427868
Refereed DesignationRefereed