Applications of reinforcement learning for building energy efficiency control: A review

The wide variety of smart devices equipped in modern intelligent buildings and the increasing comfort requirements of occupants for the environment make the control of intelligent buildings important and complex. Reinforcement learning, as a class of control techniques in machine learning, has been explored for its potential in the field of intelligent building control. Reinforcement learning methods applied to intelligent buildings can effectively reduce energy consumption. In this paper, we classify reinforcement learning algorithms and analyze the control problems that each algorithm is suitable for solving. In addition, we review the reinforcement learning methods applied to control and manage buildings, outline the problems and future directions of reinforcement learning applications in intelligent buildings, and give our suggestions for researchers who want to use reinforcement learning methods to solve control problems in this field.

Automated summary

This paper introduces the application of reinforcement learning in intelligent building control, classifies different reinforcement learning algorithms, and analyzes the control problems that each algorithm is suitable for solving. It also reviews existing research, outlines the problems and future directions of reinforcement learning applications in intelligent buildings, and provides suggestions for researchers in this field.