Interpretable Multi-Agent Path Finding via Decision Tree Extraction from Neural Policies

Thibault Lahire

Keywords: Multi-Agent Path Finding, Interpretable Reinforcement Learning, Decision Tree

TL;DR: We distill a deep reinforcement learning policy for Multi-Agent Path Finding into a decision tree, preserving performance while making agent behavior interpretable.

Abstract: Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and logistics, where multiple agents must reach their goals without collisions. While deep Multi-Agent Reinforcement Learning methods have recently shown impressive scalability and adaptability, their black-box nature hinders interpretability and trust—crucial aspects for deployment in real-world systems. In this work, we propose an interpretable policy distillation framework for MAPF. We first formulate MAPF as a stochastic game and execute a trained neural policy across diverse environments to build a large dataset of state–action pairs. We then distill this neural policy into a decision tree model that captures its underlying decision rules while maintaining strong performance. Through extensive evaluation, we analyze the trade-off between interpretability and performance, demonstrating that our distilled models achieve high fidelity to the original policy while providing transparent, human-understandable reasoning about agent behavior.

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Submission Number: 4