Graph Signal Sampling via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach using gradient multi-armed bandits to optimize sampling strategies for clustered graph signals, demonstrating improved performance over existing methods.

Contribution

It formulates graph signal sampling as a multi-armed bandit problem and applies gradient ascent to learn effective sampling strategies, a novel approach in this context.

Findings

Gradient MAB-based sampling outperforms existing methods

Learning sampling strategies improves recovery accuracy

Numerical experiments validate the approach

Abstract

We formulate the problem of sampling and recovering clustered graph signal as a multi-armed bandit (MAB) problem. This formulation lends naturally to learning sampling strategies using the well-known gradient MAB algorithm. In particular, the sampling strategy is represented as a probability distribution over the individual arms of the MAB and optimized using gradient ascent. Some illustrative numerical experiments indicate that the sampling strategies based on the gradient MAB algorithm outperform existing sampling methods.