Semi-analytical Industrial Cooling System Model for Reinforcement Learning

Yuri Chervonyi, Praneet Dutta, Piotr Trochim, Octavian Voicu, Cosmin Paduraru +7 more
7/26/2022
cs.AIcs.LGcs.RO

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Abstract

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.

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Cite this paper

@article{chervonyi2022semianalytical,
  title  = {Semi-analytical Industrial Cooling System Model for Reinforcement Learning},
  author = {Yuri Chervonyi and Praneet Dutta and Piotr Trochim and Octavian Voicu and Cosmin Paduraru and Crystal Qian and Emre Karagozler and Jared Quincy Davis and Richard Chippendale and Gautam Bajaj and Sims Witherspoon and Jerry Luo},
  year   = {2022},
  eprint = {2207.13131},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.13131v1}
}

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