# A Review of Reinforcement Learning for Autonomous Building Energy Management

arXiv: https://arxiv.org/abs/1903.05196 · HTML: https://mlsanity.com/paper/1903.05196/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 21 of 21)

- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
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- [Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety](https://mlsanity.com/paper/2105.10682/) (2021)
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- [Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments](https://mlsanity.com/paper/2103.01886/) (2021)
- [A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings](https://mlsanity.com/paper/2310.08569/) (2023)
- [The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability](https://mlsanity.com/paper/2410.03756/) (2024)
- [SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning](https://mlsanity.com/paper/2412.08293/) (2024)
- [Human-AI Shared Control via Policy Dissection](https://mlsanity.com/paper/2206.00152/) (2022)
- [A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control](https://mlsanity.com/paper/2308.05711/) (2023)
- [An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control](https://mlsanity.com/paper/2401.05737/) (2024)
- [Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Model](https://mlsanity.com/paper/2410.19262/) (2024)
- [Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review](https://mlsanity.com/paper/2503.05022/) (2025)
- [Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building](https://mlsanity.com/paper/2104.12125/) (2021)
- [BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning](https://mlsanity.com/paper/2211.14744/) (2022)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL](https://mlsanity.com/paper/2504.06386/) (2025)
- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)
- [Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation](https://mlsanity.com/paper/2605.20801/) (2026)
- [The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale](https://mlsanity.com/paper/2610.18511/) (2026)

## Abstract (arXiv)

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.