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

翻译：供暖、通风与空调（HVAC）系统是商业及住宅建筑能源消耗的主要驱动因素。近期研究表明，深度强化学习（DRL）算法能够超越传统反应式控制器。然而，基于DRL的解决方案通常针对特定场景设计，缺乏标准化比较框架。为弥补这一不足，本文对多种先进DRL算法在HVAC控制中的应用进行了严格且可复现的评估，重点关注其舒适度与能源消耗表现。研究借助Sinergym框架，考察了控制器的鲁棒性、适应性及优化目标间的权衡关系。实验结果证实了SAC和TD3等DRL算法在复杂场景下的潜力，同时揭示了泛化与增量学习方面的若干挑战。