HyperQ-Opt: Q-learning for Hyperparameter Optimization

TL;DR

This paper introduces a reinforcement learning approach using Q-learning to improve hyperparameter optimization, aiming for more efficient and scalable search strategies compared to traditional methods.

Contribution

It formulates HPO as a Markov Decision Process and applies Q-learning, offering a novel policy-based framework that outperforms conventional search techniques.

Findings

Q-learning can effectively optimize hyperparameters within limited trials

Reinforcement learning approaches outperform traditional methods in efficiency

Identifies research gaps and future directions in HPO methods

Abstract

Hyperparameter optimization (HPO) is critical for enhancing the performance of machine learning models, yet it often involves a computationally intensive search across a large parameter space. Traditional approaches such as Grid Search and Random Search suffer from inefficiency and limited scalability, while surrogate models like Sequential Model-based Bayesian Optimization (SMBO) rely heavily on heuristic predictions that can lead to suboptimal results. This paper presents a novel perspective on HPO by formulating it as a sequential decision-making problem and leveraging Q-learning, a reinforcement learning technique, to optimize hyperparameters. The study explores the works of H.S. Jomaa et al. and Qi et al., which model HPO as a Markov Decision Process (MDP) and utilize Q-learning to iteratively refine hyperparameter settings. The approaches are evaluated for their ability to find optimal or near-optimal configurations within a limited number of trials, demonstrating the potential of reinforcement learning to outperform conventional methods. Additionally, this paper identifies research gaps in existing formulations, including the limitations of discrete search spaces and reliance on heuristic policies, and suggests avenues for future exploration. By shifting the paradigm toward policy-based optimization, this work contributes to advancing HPO methods for scalable and efficient machine learning applications.