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SVSP introduces a novel approach to distilling black-box reinforcement learning policies into compact, human-interpretable subpolicies using linear support vector machine splits. The method demonstrates significant efficiency gains, improving mean return by 7.4% over prior partitioning techniques while reducing the required number of subpolicies by over 80%. This work advances the field by enabling more flexible distillation where decision boundaries and surrogate models can be optimized within a margin of the original policy behavior.

Sursă care acoperăarXiv

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

arXiv10 iunie 20262 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2605.04254v3 Announce Type: replace 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.

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.

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2605.04254 [cs.LG]

(or arXiv:2605.04254v3 [cs.LG] for this version)

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