GoKawiilBackground
Playtesting is one of the most important processes in game development. It helps with finding bugs, evaluating the game's UX, balancing the game, and, most importantly, assessing how fun it is. This is where a developer determines whether a game idea is good and discovers flaws to fix in their game. It is an iterative process that needs to be ongoing throughout the project, but it can be very time-consuming, and there are no reliable tools that automate some of this process.
PEAK aims to help with that problem, but by no means does it aim to replace playtesters. The idea behind the project is to develop a game engine with integrated deep reinforcement learning (DRL) agents that a designer can dispatch to test their game at any point.
GUI Dashboard for PEAK
The workflow of a designer using PEAK would be:
Design a new level using the level editor within PEAK.
Train DRL agents on that level, choosing the agent playstyle you want to test with.
The agent would gather data, and based on your defined thresholds, it would tell you how balanced the level is and point out if there are issues within it, such as a difficulty spike on one of the platforms.
Balancing the Pipeline of PEAK
PEAK is developed by Cristiano Politowski, Al Shifan, and Kevin Chua at Ontario Tech University. I only joined this project at a later stage. It currently only handles 2D platformer games, maybe even Metroidvanias, since they share many similarities. That scope can be expanded later on, but for now, 2D platformers are used as a proof of concept.
... continue reading