# Fully Distributed Bayesian Optimization with Stochastic Policies

## TL;DR

This paper introduces a fully distributed Bayesian optimization method that leverages stochastic policies like the Boltzmann policy, enabling high parallelism and scalability for high-throughput computing tasks.

## Contribution

The paper proposes a novel distributed Bayesian optimization framework using stochastic policies, compatible with any acquisition function, and models the process as a partially observable Markov decision process.

## Key findings

- Demonstrates high scalability and parallelism in benchmarks
- Outperforms traditional sequential and parallel methods
- Effective in hyperparameter tuning and computer experiment design

## Abstract

Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mostly sequential. Even parallel variants require certain computations between samples, limiting the parallelization bandwidth. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that shows the performance of our method.