mlsanityData to 7 Oct 2026

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

arXiv 2203.05434 ↗First posted 10 Mar 2022cs.LG cs.AI cs.SYcited by 4

Not on the map. Why?

Abstract (arXiv)

Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement Learning (DRL) have been shown to be more effective than conventional baselines. However, since the optimal solution is usually unknown, it is still unclear if DRL agents are attaining near-optimal performance in general or if there is still a large gap to bridge. In this paper, we investigate the performance of DRL agents compared to the theoretically optimal solution. To that end, we leverage Physically Consistent Neural Networks (PCNNs) as simulation environments, for which optimal control inputs are easy to compute. Furthermore, PCNNs solely rely on data to be trained, avoiding the difficult physics-based modeling phase, while retaining physical consistency. Our results hint that DRL agents not only clearly outperform conventional rule-based controllers, they furthermore attain near-optimal performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 10 Mar 2022.

Builds on

9 of 22 references.

References · 9

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  5. 2017Deep Reinforcement Learning that Matterscited by 727
  6. 2021Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimizationcited by 31
  7. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 16
  8. 2018Deep Reinforcement Learning for Optimal Control of Space Heatingcited by 7
  9. 2021Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experimentscited by 7

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1.

4 papers.

  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2022Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Modelscited by 4
  3. 2022Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learningcited by 1
  4. 2022Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rulescited by 0