用超网络实现空调系统持续学习，少调参就能快速适应新环境。
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
- 用超网络动态建模不同空调系统的环境变化，提升采样效率。
- 在新任务上仅需5次试错就快速收敛，比传统方法快得多。
- 适合需要长期运行且频繁切换场景的智能建筑管理系统。
建筑中的暖通空调（HVAC）系统对室内舒适度与能效至关重要。传统依赖物理模型，而大数据时代催生了深度强化学习（DRL）等数据驱动方法。然而，强化学习常面临样本效率低、跨系统泛化差的问题。本文提出一种基于模型的强化学习框架，采用超网络（Hypernetwork）在不同动作空间的任务间持续学习环境动态，实现高效合成轨迹生成与样本利用。实验表明，在连续学习设置中完成第二任务训练后，对第一任务进行极小微调即可在仅5个回合内快速收敛，显著优于无模型强化学习（MFRL），有效缓解灾难性遗忘。该方法可大幅降低建筑能耗与运维成本，助力全球可持续发展目标。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。