Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.

翻译：强化学习（RL）是优化暖通空调（HVAC）控制的一种有前景的方法。RL提供了一个框架，用于改善系统性能、降低能耗并提高成本效益。我们在多种HVAC环境下对两种流行的经典与深度RL方法（Q-Learning和Deep-Q-Networks）进行了基准测试，并探讨了模型超参数选择与奖励调优的实践考量。研究结果可为HVAC系统中RL代理的配置提供见解，从而推动节能且经济高效的运行。