# A Review of Reinforcement Learning for Autonomous Building Energy Management

## TL;DR

This paper reviews how reinforcement learning has been applied to autonomous building energy management, highlighting recent advances, challenges, and future research directions in optimizing energy use with intelligent control algorithms.

## Contribution

It provides a comprehensive overview of reinforcement learning applications in building energy management and discusses future challenges and research opportunities.

## Key findings

- Reinforcement learning has been successfully applied to optimize building energy systems.
- The review identifies key challenges and future directions in RL-based energy management.
- The paper summarizes recent advancements and gaps in the literature.

## Abstract

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.