mlsanityData to 7 Oct 2026

Low Emission Building Control with Zero-Shot Reinforcement Learning

arXiv 2208.06385 ↗First posted 12 Aug 2022cs.LG cs.SY eess.SYcited by 3

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Abstract (arXiv)

Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori--a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort. Our source code is available online via https://enjeeneer.io/projects/pearl .

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 12 Aug 2022.

Builds on

17 of 41 references.

References · 17

  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. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  6. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
  7. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 466
  8. 2013Gaussian Processes for Big Datacited by 377
  9. 2022Temporal Difference Learning for Model Predictive Controlcited by 375
  10. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
  11. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 288
  12. 2013Zero-Shot Learning Through Cross-Modal Transfercited by 237
5 more
  1. 2015Model Predictive Path Integral Control using Covariance Variable Importance Samplingcited by 122
  2. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 35
  3. 2019Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximationcited by 14
  4. 2018Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learningcited by 9
  5. 2020NeurOpt: Neural network based optimization for building energy management and climate controlcited by 5

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Zero-Shot Reinforcement Learning from Low Quality Datacited by 21
  2. 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2
  3. 2025Decision-Focused Learning for Complex System Identification: HVAC Management System Applicationcited by 0