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

2026-05-05

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.

Problem

• black box reinforcement learning
• policy distillation
• interpretable subpolicies
• state partitioning
• support vector machine

Innovation

• State Vector Space Partitioning
• Support Vector Machine
• Policy Distillation
• Interpretable Subpolicies
• Reinforcement Learning