Gregor P. Henze

About

Gregor P. Henze is a leading authority in the application of advanced machine learning to building energy systems, with a particular focus on reinforcement learning (RL) and inverse reinforcement learning (IRL). His work addresses the growing complexity of building energy management by developing intelligent control strategies that optimize energy efficiency without sacrificing occupant comfort. Henze’s major contributions include pioneering the use of imitation learning to bootstrap RL controllers, significantly reducing the training time and data requirements that have historically hindered real-world deployment. His most-cited paper, "Reinforcement learning building control approach harnessing imitation learning" (2023, 54 citations), demonstrates how RL can achieve superior performance in sequential decision-making for building systems, drawing parallels to its successes in autonomous vehicles and robotics. Further, his research on inverse reinforcement learning (2023, 26+ citations) provides a framework for learning optimal control policies directly from expert demonstrations, enabling more intuitive and adaptable building automation. With a career dedicated to bridging the gap between theoretical AI and practical building operations, Henze’s work is shaping the next generation of smart, sustainable infrastructure.

Research Focus

Key Achievements

Top Papers

1. 1
   Reinforcement learning building control approach harnessing imitation learning

   54 citations · 2023
2. 2
   Inverse reinforcement learning control for building energy management

   26 citations · 2023
3. 3
   Inverse Reinforcement Learning Control for Building Energy Management

   3 citations · 2023

Key Collaborators

Contact & Links