Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning

@article{Wong2022OptimizingIH,
  title={Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning},
  author={William Wong and Praneet Dutta and Octavian Voicu and Yuri Chervonyi and Cosmin Paduraru and Jerry Luo},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.08112}
}
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… 
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