Reinforcement learning building control approach harnessing imitation learning

作     者：Sourav Dey Thibault Marzullo Xiangyu Zhang Gregor Henze 

作者机构：Department of CivilEnvironmental and Architectural EngineeringUniversity of ColoradoBoulderCOUSA National Renewable Energy LaboratoryGoldenCOUSA Renewable and Sustainable Energy InstituteBoulderCOUSA 

出 版 物：《Energy and AI》 (能源与人工智能(英文))

年 卷 期：2023年第14卷第4期

页      面：60-72页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was authored in part by the National Renewable Energy Laboratory United States operated by Alliance for Sustainable Energy LLC for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308. 

主　　题：Reinforcement learning Building controls Imitation learning Artificial intelligence 

摘      要：Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple,potentially conflicting,goals like occupant comfort,energy use and grid interactivity.However,for real world applications,RL has several drawbacks like requiring large training data and time,and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks.To address these issues,an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies.This approach is successful in reducing the training time,preventing the unstable early exploration behavior and improving upon an accepted rule-based policy-all of these make RL a more practical control approach for real world applications in the domain of building controls.