Semi-analytical Industrial Cooling System Model for Reinforcement Learning

07/26/2022

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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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Semi-analytical Industrial Cooling System Model for Reinforcement Learning

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning

OR-Gym: A Reinforcement Learning Library for Operations Research Problem

Multi-Fidelity Reinforcement Learning with Gaussian Processes

A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

A Benchmark Environment Motivated by Industrial Control Problems

An Architecture for Deploying Reinforcement Learning in Industrial Environments

Semi-analytical Industrial Cooling System Model for Reinforcement Learning

Related Research

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning

OR-Gym: A Reinforcement Learning Library for Operations Research Problem

Multi-Fidelity Reinforcement Learning with Gaussian Processes

A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

A Benchmark Environment Motivated by Industrial Control Problems

An Architecture for Deploying Reinforcement Learning in Industrial Environments