Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
This work addresses the need for transparent decision-making in reinforcement learning for energy management companies, though it appears incremental as it builds on existing soft differential decision tree techniques.
The paper tackled the problem of black-box deep reinforcement learning in home energy management by proposing an asymmetric soft differentiable decision tree method, which improves interpretability and performance through adaptive node expansion.
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.