Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning
This work addresses energy efficiency for industrial cooling systems, but it is incremental as it builds on existing hierarchical RL methods applied to a specific domain.
The paper tackles the problem of optimizing industrial HVAC systems by addressing real-world machinery constraints, such as varying action time scales, using hierarchical reinforcement learning with multiple agents, achieving energy savings over existing baselines in a simulated environment.
Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A major challenge in industrial control involves learning behaviors that are feasible in the real world due to machinery constraints. For example, certain actions can only be executed every few hours while other actions can be taken more frequently. Without extensive reward engineering and experimentation, an RL agent may not learn realistic operation of machinery. To address this, we use hierarchical reinforcement learning with multiple agents that control subsets of actions according to their operation time scales. Our hierarchical approach achieves energy savings over existing baselines while maintaining constraints such as operating chillers within safe bounds in a simulated HVAC control environment.