Semi-analytical Industrial Cooling System Model for Reinforcement Learning
This work addresses the need for interpretable and efficient simulation models for RL applications in industrial cooling systems, representing an incremental advancement in domain-specific modeling.
The authors tackled the problem of modeling industrial cooling systems for reinforcement learning by developing a hybrid model that embeds analytical solutions in a multi-physics simulation, which was evaluated against real-world data and used in a case study to test RL algorithms on a new task suite.
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.