A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

TL;DR

This paper compares classical and deep reinforcement learning methods for HVAC control, benchmarking their performance across various environments to guide practical implementation for energy efficiency and cost savings.

Contribution

It provides a comparative analysis of Q-Learning and Deep-Q-Networks for HVAC control, including insights on hyper-parameter and reward tuning for real-world application.

Findings

Deep-Q-Networks outperform Q-Learning in complex environments.

Proper hyper-parameter tuning significantly improves RL performance.

Guidelines for configuring RL agents in HVAC systems are provided.

Abstract

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.