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Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Toon Van Puyvelde, Mehran Zareh, Chris Develder
https://arxiv.org/abs/2506.01641

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
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 promisin…