Entropy-Based Logic Explanations of Differentiable Decision Tree

Explainable reinforcement learning has evolved rapidly over the years because transparency of the model’s decision-making process is crucial in some important domains. Differentiable decision trees have been applied to this field due to their performance and interpretability. However, the number of parameters per branch node of a differentiable decision tree is related to the state dimension. When the feature dimension of states increases, the number of states considered by the model in each branch node decision also increases linearly, which increases the difficulty of human understanding. This paper proposes a entroy-based differentiable decision tree, which can restrict each branch node to use as few features as possible to predict during the training process. After the training is completed, the parameters that have little impact on the output of the branch node will be blocked, thus significantly reducing the decision complexity of each branch node. Experiments in multiple environments demonstrate the significant interpretability advantage of our proposed approach.