Reinforcement Learning for telecommunication
Reinforcement learning (RL) for HVAC and refrigeration focuses on using adaptive learning techniques to optimize energy efficiency, comfort, and system performance in buildings. RL enables HVAC and refrigeration systems to learn optimal control strategies, adapting to changing environmental conditions and occupancy patterns. Our research aims to advance RL methods tailored for these applications, enhancing energy savings, indoor air quality, and overall system reliability. Explore our work to discover innovative approaches driving the evolution of smart HVAC and refrigeration systems leveraging reinforcement learning.
Our latest publications in this research area
2017
People Involved
Gian Antonio Susto
Associate Professor
gianantonio.susto@unipd.itRL algorithms, Rl for manufacturing, RL for smart mobility, RL for robotics, RL for telecommunication, RL for HVAC&R
