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

arXiv: https://arxiv.org/abs/2605.04254 · HTML: https://mlsanity.com/paper/2605.04254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 6 of 6)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Verifiable Reinforcement Learning via Policy Extraction](https://mlsanity.com/paper/1805.08328/) (2018)
- [Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning](https://mlsanity.com/paper/2405.14956/) (2024)
- [Multiclass Optimal Classification Trees with SVM-splits](https://mlsanity.com/paper/2111.08674/) (2021)
- [Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution](https://mlsanity.com/paper/2410.21940/) (2024)
- [Explainable RL Policies by Distilling to Locally-Specialized Linear Policies with Voronoi State Partitioning](https://mlsanity.com/paper/2511.13322/) (2025)

## Abstract (arXiv)

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.