Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies

Senne Deproost; Mehrdad Asadi; Ann Nowé

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arXiv · 2605.04254

Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies

Senne Deproost·Mehrdad Asadi·Ann Nowé

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Abstract

We introduce State Vector Space Partitioning (SVSP), a novel method to mimic a black box reinforcement learning policy using a set of human-interpretable subpolicies. By partitioning a distillation dataset of state action pairs with linear support vector machine splits, SVSP constructs a compact and structured representation of the original policy. Our method improves mean return by +7.4% over previous critic driven state partitioning attempts such as Voronoi State Partitioning (VSP) and +2.8% over the original TD3 policy, while reducing the number of required subpolicies against VSP by 82.1%. Our results pave the path towards a more flexible form of distillation where both the decision boundary and surrogate models can be chosen within a margin of the original black box behavior.

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Senne Deproost, Mehrdad Asadi, Ann Nowé. 2026-05-05. Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies. https://arxiv.org/abs/2605.04254

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