mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep Neural Networks

TL;DR

mPOLICE is a novel method that extends the POLICE approach to enforce affine constraints across multiple disjoint convex regions in deep neural networks, ensuring safety and correctness in critical applications.

Contribution

It introduces a layer-wise optimization and training algorithm for provably enforcing multi-region affine constraints without runtime overhead.

Findings

Effective in safety-critical reinforcement learning tasks

Successfully enforces geometric constraints in 3D shape modeling

Maintains performance while ensuring boundary conditions in fluid simulations

Abstract

Deep neural networks are increasingly used in safety-critical domains such as robotics and scientific modeling, where strict adherence to output constraints is essential. Methods like POLICE, which are tailored for single convex regions, face challenges when extended to multiple disjoint regions, often leading to constraint violations or unwanted affine behavior across regions. This paper proposes mPOLICE, a new approach that generalizes POLICE to provably enforce affine constraints over multiple disjoint convex regions. At its core, mPOLICE assigns distinct neuron activation patterns to each constrained region, enabling localized affine behavior and avoiding unintended generalization. This is implemented through a layer-wise optimization of the network parameters. Additionally, we introduce a training algorithm that incorporates mPOLICE into conventional deep learning pipelines, balancing task-specific performance with constraint enforcement using periodic sign pattern enforcement. We validate the flexibility and effectiveness of mPOLICE through experiments across various applications, including safety-critical reinforcement learning, implicit 3D shape representation with geometric constraints, and fluid dynamics simulations with boundary condition enforcement. Importantly, mPOLICE incurs no runtime overhead during inference, making it a practical and reliable solution for constraint handling in deep neural networks.