Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies
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
The increasing complexity of black-box AI models necessitates novel methods for interpretability and explainability, especially for deployment in sensitive or critical applications.
This development offers a significant step towards making advanced reinforcement learning policies more transparent and auditable, crucial for trust and widespread adoption in real-world systems.
Policies previously opaque can now be distilled into human-interpretable subpolicies, enabling better understanding, debugging, and potential modification of complex AI behaviors.
- · AI developers focused on interpretability
- · Industries requiring explainable AI (e.g., finance, healthcare, defense)
- · Regulatory bodies developing AI governance frameworks
- · Developers relying solely on black-box model performance without interpretabilit
- · Systems where opacity is an unintended feature or accepted limitation
Reinforcement learning policies become more deployable in high-stakes environments due to increased interpretability.
Improved interpretability accelerates the adoption of autonomous AI agents across various sectors, as their decision-making processes can be understood and vetted.
The ability to distill complex policies into interpretable rules could lead to new avenues for human-AI collaboration and the development of 'hybrid intelligence' systems.
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