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.

本文介绍了一个混合型工业冷却系统模型，该模型在多物理模拟中嵌入了分析解，并设计用于强化学习应用，平衡简单性与模拟保真度和可解释性。该模型的保真度通过来自大型冷却系统的实际数据进行评估。随后，通过案例研究，说明了如何使用该模型进行RL研究。对此，我们开发了一项工业任务套件，允许指定不同的问题设置和复杂性水平，并使用它来评估不同RL算法的性能。

半解析工业冷却系统模型在强化学习中的应用