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A Review of Reinforcement Learning for Autonomous Building Energy Management

arXiv 1903.05196 ↗First posted 12 Mar 2019cs.LG cs.SY eess.SYcited by 21

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 2; 2024: 5; 2025: 3; 2026: 2.

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  1. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  2. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  3. 2021Safe Driving via Expert Guided Policy Optimizationcited by 31
  4. 2021Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safetycited by 30
  5. 2024CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communitiescited by 15
  6. 2021Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experimentscited by 8
  7. 2023A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildingscited by 5
  8. 2024The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainabilitycited by 5
  9. 2024SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learningcited by 5
  10. 2022Human-AI Shared Control via Policy Dissectioncited by 4
  11. 2023A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Controlcited by 4
  12. 2024An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC controlcited by 4
9 more
  1. 2024Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Modelcited by 3
  2. 2025Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A reviewcited by 3
  3. 2021Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Buildingcited by 2
  4. 2022BEAR: Physics-Principled Building Environment for Control and Reinforcement Learningcited by 1
  5. 2022Machine Learning for Smart and Energy-Efficient Buildingscited by 1
  6. 2025SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RLcited by 1
  7. 2025EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inferencecited by 0
  8. 2026Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigationcited by 0
  9. 2026The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scalecited by 0