# Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access

## TL;DR

This paper introduces a kernel-based reinforcement learning method with a budget-constrained sparsification technique for efficient spectrum access, improving throughput in congested wireless environments.

## Contribution

It presents a novel nonparametric RL approach with a sparsification method that scales to large state spaces for opportunistic spectrum access.

## Key findings

- Performance gains over carrier-sense systems
- Effective learning in large state spaces
- Successful coexistence with adversarial radios

## Abstract

Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the environment to find the best channel access actions. This approach allows learning and planning over the intrinsic state-action space and extends well to large state spaces. We apply our methods to evaluate coexistence of a reinforcement learning-based radio with a multi-channel adversarial radio and a single-channel CSMA-CA radio. Numerical experiments show the performance gains over carrier-sense systems.