标题： 可解释的热泵控制强化学习通过非对称可微决策树 显示英文标题

标题： Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees

摘要： 近年来，深度强化学习（DRL）算法在家庭能源管理系统中引起了广泛关注。然而，由于DRL的黑箱性质，未能提供透明的决策反馈，能源管理公司对其采用仍受到限制。为了解决这一问题，可解释强化学习（XRL）技术应运而生，旨在使DRL决策更加透明。在这其中，软微分决策树（DDT）蒸馏提供了一种有前景的方法，因为它们基于清晰的决策规则，可以高效计算。 然而，实现高性能通常需要构建深度且完全满的树，这降低了可解释性。为了解决这个问题，我们提出了一种新颖的非对称软DDT构造方法。与传统的软DDT不同，我们的方法通过仅在必要时扩展节点来自适应地构建树。这种方法提高了决策节点的有效利用，这些节点需要预先确定的深度来构建完整的对称树，从而同时提升了可解释性和性能。 我们展示了非对称DDT在家庭能源管理系统中提供透明、高效和高性能决策的潜力。 显示英文摘要

摘要： 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.