Hybrid Reinforcement Learning Framework for Mixed-Variable Problems

TL;DR

This paper presents a hybrid reinforcement learning framework that combines RL for discrete decisions with Bayesian Optimization for continuous parameters, effectively solving complex mixed-variable optimization problems.

Contribution

It introduces a novel integrated approach that leverages RL and Bayesian Optimization to improve performance on mixed-variable problems.

Findings

Outperforms traditional RL, random search, and standalone Bayesian optimization.

Demonstrates effectiveness on synthetic functions and real-world hyperparameter tuning.

Enhances optimization efficiency and adaptability in mixed-variable spaces.

Abstract

Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating mixed-variable spaces effectively. To Address these challenges, we introduce a hybrid Reinforcement Learning (RL) framework that synergizes RL for discrete variable selection with Bayesian Optimization for continuous variable adjustment. This framework stands out by its strategic integration of RL and continuous optimization techniques, enabling it to dynamically adapt to the problem's mixed-variable nature. By employing RL for exploring discrete decision spaces and Bayesian Optimization to refine continuous parameters, our approach not only demonstrates flexibility but also enhances optimization performance. Our experiments on synthetic functions and real-world machine learning hyperparameter tuning tasks reveal that our method consistently outperforms traditional RL, random search, and standalone Bayesian optimization in terms of effectiveness and efficiency.