A New Method for Parallel Monte Carlo Tree Search

TL;DR

This paper introduces a novel parallel Monte Carlo tree search method utilizing pipeline computation to improve performance on large parallel systems, addressing previous scalability challenges.

Contribution

It presents a new parallelization approach for Monte Carlo tree search based on pipeline computation, enhancing scalability and efficiency.

Findings

Improved scalability on large parallel machines

Enhanced efficiency of Monte Carlo tree search

Addresses previous parallelization limitations

Abstract

In recent years there has been much interest in the Monte Carlo tree search algorithm, a new, adaptive, randomized optimization algorithm. In fields as diverse as Artificial Intelligence, Operations Research, and High Energy Physics, research has established that Monte Carlo tree search can find good solutions without domain dependent heuristics. However, practice shows that reaching high performance on large parallel machines is not so successful as expected. This paper proposes a new method for parallel Monte Carlo tree search based on the pipeline computation pattern.