mlsanityData to 7 Oct 2026

An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

arXiv 2401.05737 ↗First posted 11 Jan 2024cs.LG cs.SY eess.SYcited by 3

Not on the map. Why?

Abstract (arXiv)

Heating, Ventilation, and Air Conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can outperform traditional reactive controllers. However, DRL-based solutions are generally designed for ad hoc setups and lack standardization for comparison. To fill this gap, this paper provides a critical and reproducible evaluation, in terms of comfort and energy consumption, of several state-of-the-art DRL algorithms for HVAC control. The study examines the controllers' robustness, adaptability, and trade-off between optimization goals by using the Sinergym framework. The results obtained confirm the potential of DRL algorithms, such as SAC and TD3, in complex scenarios and reveal several challenges related to generalization and incremental learning.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 100 references that are in the map. First posted on arXiv 11 Jan 2024.

Builds on

21 of 100 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2015Trust Region Policy Optimizationcited by 2,503
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  8. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  9. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 361
  10. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 141
  11. 2017Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Controlcited by 92
  12. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 35
9 more
  1. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  2. 2020Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildingscited by 24
  3. 2017Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learningcited by 18
  4. 2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
  5. 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 18
  6. 2020One for Many: Transfer Learning for Building HVAC Controlcited by 16
  7. 2019Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximationcited by 14
  8. 2015Experimental analysis of data-driven control for a building heating systemcited by 7
  9. 2021Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Controlcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learningcited by 3
  2. 2026ThermoLLM: Thermodynamics-Aware HVAC Control with Spatial-Semantic Knowledge Graphcited by 2
  3. 2026Physics-enhanced reinforcement learning for real-time optimal control of dynamical systemscited by 0