Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models

2026-05-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new model-agnostic method, Critic-Driven Voronoi State Partitioning, addresses the performance-interpretability trade-off in distilling Deep Reinforcement Learning (DRL) policies into explainable models. This approach partitions a black-box control policy into regions where simpler models can be optimized via gradient descent. Unlike traditional distillation, which only minimizes behavioral distance, this method exploits the original policy's critic value network to iteratively introduce new subpolicies in regions of insufficient value, effectively measuring policy complexity. The partitioning uses a Voronoi quantizer with nearest neighbor lookups, assigning a linear function to each state space point, creating a cell-like diagram. The method has been validated on several benchmarks, demonstrating its ability to approximate the original policy with a reasonably sized set of linear functions.

Key takeaway

For research scientists developing explainable AI for Deep Reinforcement Learning, this method offers a robust way to balance model performance with interpretability. You should consider integrating Critic-Driven Voronoi State Partitioning to create surrogate models that not only mimic behavior but also account for action value, leading to more transparent and verifiable DRL systems.

Key insights

Critic-Driven Voronoi State Partitioning distills DRL policies into explainable models by leveraging critic values for state space partitioning.

Principles

• Exploit critic value networks for policy complexity.
• Iteratively refine subpolicies in low-value regions.

Method

Partition black-box DRL policies using a Voronoi quantizer, assigning linear functions to state space regions, and iteratively optimizing subpolicies based on critic value.

In practice

• Distill complex DRL policies into simpler, linear models.
• Balance performance and interpretability in RL systems.

Topics

• Deep Reinforcement Learning
• Explainable AI
• Policy Distillation
• Critic-Driven Voronoi State Partitioning
• Voronoi Quantizer

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.