mlsanityData to 7 Oct 2026

Semi-analytical Industrial Cooling System Model for Reinforcement Learning

arXiv 2207.13131 ↗First posted 26 Jul 2022cs.AI cs.LG cs.ROcited by 9

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Abstract (arXiv)

We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpretability. The model's fidelity is evaluated against real world data from a large scale cooling system. This is followed by a case study illustrating how the model can be used for RL research. For this, we develop an industrial task suite that allows specifying different problem settings and levels of complexity, and use it to evaluate the performance of different RL algorithms.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

14 of 41 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2016OpenAI Gymcited by 1,684
  4. 2018DeepMind Control Suitecited by 935
  5. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 350
  6. 2018Maximum a Posteriori Policy Optimisationcited by 318
  7. 2019Challenges of Real-World Reinforcement Learningcited by 282
  8. 2018Distributed Distributional Deterministic Policy Gradientscited by 195
  9. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  10. 2018Optimal Control Via Neural Networks: A Convex Approachcited by 82
  11. 2021DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learningcited by 47
  12. 2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
2 more
  1. 2019Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learningcited by 8
  2. 2021Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Controlcited by 5

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 1.

9 papers.

  1. 2022Controlling Commercial Cooling Systems Using Reinforcement Learningcited by 25
  2. 2023Efficient Policy Evaluation with Offline Data Informed Behavior Policy Designcited by 12
  3. 2024Efficient Multi-Policy Evaluation for Reinforcement Learningcited by 8
  4. 2024Doubly Optimal Policy Evaluation for Reinforcement Learningcited by 7
  5. 2024Efficient Policy Evaluation with Safety Constraint for Reinforcement Learningcited by 5
  6. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learningcited by 4
  7. 2022Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learningcited by 2
  8. 2023Anytime-Competitive Reinforcement Learning with Policy Priorcited by 1
  9. 2024Interactive Design-of-Experiments: Optimizing a Cooling Systemcited by 0