面向HVAC系统控制的持续强化学习：集成超网络与迁移学习
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
AI总结：
针对HVAC系统控制，提出基于超网络的模型强化学习框架，实现跨任务持续学习，5回合内快速收敛并缓解灾难性遗忘，优于无模型方法。
摘要：
配备供暖、通风与空调（HVAC）系统的建筑在确保室内舒适度和能效方面发挥着至关重要的作用。尽管传统上这些系统由基于物理的模型控制，但大数据时代的到来使得深度强化学习（DRL）等数据驱动方法得以应用。然而，基于强化学习（RL）的技术常常面临样本效率低下和泛化能力有限的问题，尤其是在不同HVAC系统之间。我们提出了一种基于模型的强化学习框架，该框架利用超网络（Hypernetwork）在具有不同动作空间的任务间持续学习环境动态。这实现了高效的合成轨迹生成和更好的样本利用率。我们的方法在持续学习环境中，在完成第二个任务的训练后展现出强大的反向迁移能力，对第一个任务进行极少的微调即可在仅5个回合内快速收敛，从而优于无模型强化学习（MFRL），并有效缓解了灾难性遗忘。这些发现对于降低建筑管理中的能耗和运营成本具有重要意义，从而支持全球可持续发展目标。
英文摘要：
Buildings with Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in ensuring indoor comfort and efficiency. While traditionally governed by physics-based models, the emergence of big data has enabled data-driven methods like Deep Reinforcement Learning (DRL). However, Reinforcement Learning (RL)-based techniques often suffer from sample inefficiency and limited generalization, especially across varying HVAC systems. We introduce a model-based reinforcement learning framework that uses a Hypernetwork to continuously learn environment dynamics across tasks with different action spaces. This enables efficient synthetic rollout generation and improved sample usage. Our approach demonstrates strong backward transfer in a continual learning setting after training on a second task, minimal fine-tuning on the first task allows rapid convergence within just 5 episodes and thus outperforming Model Free Reinforcement Learning (MFRL) and effectively mitigating catastrophic forgetting. These findings have significant implications for reducing energy consumption and operational costs in building management, thus supporting global sustainability goals. Keywords: Deep Reinforcement Learning, HVAC Systems Control, Hypernetworks, Transfer and Continual Learning, Catastrophic Forgetting
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
