Automating Speedrun Routing: Overview and Vision

TL;DR

This paper provides an overview of speedrun routing, discusses modeling challenges, and explores optimization methods including metaheuristics and deep learning for automating speedrun planning.

Contribution

It introduces a structured framework for speedrun routing, reviews existing literature, and assesses potential optimization approaches for automating speedrun strategies.

Findings

Identifies key challenges in speedrun routing modeling.

Proposes graph-based representations for the problem.

Evaluates applicability of metaheuristics and deep learning methods.

Abstract

Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods.