Robust Bayesian optimization with reinforcement learned acquisition functions

TL;DR

This paper introduces a reinforcement learning-based method for dynamically selecting acquisition functions in Bayesian optimization, improving efficiency and robustness in black-box optimization tasks.

Contribution

It formalizes the AF selection as an MDP and employs RL to learn an adaptive policy, enhancing BO performance over traditional static approaches.

Findings

RLABO outperforms baseline methods on benchmark problems.

The learned policy effectively balances exploration and exploitation.

RL-based AF selection demonstrates robustness and practical potential.

Abstract

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually rely on artificial experiences in terms of preferences for exploration or exploitation, which runs a risk of a computational waste or traps in local optima and resultant re-optimization. To address the crux, the idea of data-driven AF selection is proposed, and the sequential AF selection task is further formalized as a Markov decision process (MDP) and resort to powerful reinforcement learning (RL) technologies. Appropriate selection policy for AFs is learned from superior BO trajectories to balance between exploration and exploitation in real time, which is called reinforcement-learning-assisted Bayesian optimization (RLABO). Competitive and robust BO evaluations on five benchmark problems demonstrate RL's recognition of the implicit AF selection pattern and imply the proposal's potential practicality for intelligent AF selection as well as efficient optimization in expensive black-box problems.