Developing a Successful Bomberman Agent

TL;DR

This paper compares search algorithms for a Bomberman AI, introducing enhancements that improve agent performance, culminating in a top-ranked agent on the CodinGame platform.

Contribution

It presents a comprehensive analysis and enhancement of search-based AI methods for Bomberman, achieving state-of-the-art performance.

Findings

Beam Search with bit-based state representation outperforms other algorithms

Enhanced agent strategies lead to top placement among 2,300 submissions

Simulation-based evaluation effectively prunes unpromising states

Abstract

In this paper, we study AI approaches to successfully play a 2-4 players, full information, Bomberman variant published on the CodinGame platform. We compare the behavior of three search algorithms: Monte Carlo Tree Search, Rolling Horizon Evolution, and Beam Search. We present various enhancements leading to improve the agents' strength that concern search, opponent prediction, game state evaluation, and game engine encoding. Our top agent variant is based on a Beam Search with low-level bit-based state representation and evaluation function heavy relying on pruning unpromising states based on simulation-based estimation of survival. It reached the top one position among the 2,300 AI agents submitted on the CodinGame arena.