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arXiv Open Access 2022

Semi-analytical Industrial Cooling System Model for Reinforcement Learning

Yuri Chervonyi Praneet Dutta Piotr Trochim Octavian Voicu Cosmin Paduraru +7 lainnya
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Abstrak

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.

Topik & Kata Kunci

cs.AI cs.LG cs.RO

Penulis (12)

Y

Yuri Chervonyi

P

Praneet Dutta

P

Piotr Trochim

O

Octavian Voicu

C

Cosmin Paduraru

C

Crystal Qian

E

Emre Karagozler

J

Jared Quincy Davis

R

Richard Chippendale

G

Gautam Bajaj

S

Sims Witherspoon

J

Jerry Luo

Format Sitasi

APA MLA BibTeX
Chervonyi, Y., Dutta, P., Trochim, P., Voicu, O., Paduraru, C., Qian, C. et al. (2022). Semi-analytical Industrial Cooling System Model for Reinforcement Learning. https://arxiv.org/abs/2207.13131

Akses Cepat

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Tahun Terbit
2022
Bahasa
en
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arXiv
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Open Access ✓