用可解释的决策树提升热泵控制的透明度与效率
Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
- 设计不对称可微决策树，按需扩展节点而非全树填充
- 在家庭能源管理中实现高精度且易理解的控制策略
- 适合关注模型可解释性与实际部署的能源系统研究者
近年来，深度强化学习（DRL）在家庭能源管理系统中受到关注，但其黑箱特性限制了能源公司采用。为此，可解释强化学习（XRL）技术应运而生，其中软可微决策树（DDT）蒸馏因具备清晰决策规则而展现出潜力。然而，高性能通常依赖于深层且完全饱满的对称树结构，牺牲了可解释性。为此，本文提出一种新型非对称软DDT构建方法：不强制预设深度，仅在必要时扩展节点，从而更高效利用决策节点，兼顾可解释性与性能。实验表明，该方法可在家庭能源管理中实现透明、高效且高表现力的决策，为可解释强化学习提供了新路径。
原文摘要 · Abstract (English)
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of DRL, which fails to provide transparent decision-making feedback. To address this, explainable reinforcement learning (XRL) techniques have emerged, aiming to make DRL decisions more transparent. Among these, soft differential decision tree (DDT) distillation provides a promising approach due to the clear decision rules they are based on, which can be efficiently computed. However, achieving high performance often requires deep, and completely full, trees, which reduces interpretability. To overcome this, we propose a novel asymmetric soft DDT construction method. Unlike traditional soft DDTs, our approach adaptively constructs trees by expanding nodes only when necessary. This improves the efficient use of decision nodes, which require a predetermined depth to construct full symmetric trees, enhancing both interpretability and performance. We demonstrate the potential of asymmetric DDTs to provide transparent, efficient, and high-performing decision-making in home energy management systems.
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