# Array-Based Monte Carlo Tree Search

## TL;DR

This paper introduces an array-based implementation of Monte Carlo Tree Search that enhances computational efficiency and scalability, enabling faster simulations and improved search performance on modern processors.

## Contribution

The paper presents a novel array-based approach to Monte Carlo Tree Search that reduces branch prediction overhead and doubles scaling efficiency with search depth.

## Key findings

- Achieves up to 2.8x faster performance in simulations
- Reduces branch prediction overhead in MCTS implementations
- Improves scalability with search depth on pipelined processors

## Abstract

Monte Carlo Tree Search is a popular method for solving decision making problems. Faster implementations allow for more simulations within the same wall clock time, directly improving search performance. To this end, we present an alternative array-based implementation of the classic Upper Confidence bounds applied to Trees algorithm. Our method preserves the logic of the original algorithm, but eliminates the need for branch prediction, enabling faster performance on pipelined processors, and up to a factor of 2.8 times better scaling with search depth in our numerical simulations.