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Heating, Ventilation, and Air Conditioning (HVAC) systems are a major driverof energy consumption in commercial and residential buildings. Recent studieshave shown that Deep Reinforcement Learning (DRL) algorithms can outperformtraditional reactive controllers. However, DRL-based solutions are generallydesigned for ad hoc setups and lack standardization for comparison. To fillthis gap, this paper provides a critical and reproducible evaluation, in termsof comfort and energy consumption, of several state-of-the-art DRL algorithmsfor HVAC control. The study examines the controllers' robustness, adaptability,and trade-off between optimization goals by using the Sinergym framework. Theresults obtained confirm the potential of DRL algorithms, such as SAC and TD3,in complex scenarios and reveal several challenges related to generalizationand incremental learning.

An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control