Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

While traditionally a labour intensive task, the testing of game content is progressively becoming more automated.
Among the many directions in which this automation is taking shape, automatic play-testing is one of the most promising thanks also to advancements of many supervised and reinforcement learning (RL) algorithms.
However these type of algorithms, while extremely powerful, often suffer in production environments due to issues with reliability and transparency in their training and usage.

In this research work we are investigating and evaluating strategies to apply the popular RL method Proximal Policy Optimization (PPO) in a casual mobile puzzle game with a specific focus on improving its reliability in training and generalization during game playing.

We have implemented and tested a number of different strategies against a real-world mobile puzzle game (Lily's Garden from Tactile Games).
We isolated the conditions that lead to a failure in either training or generalization during testing and we identified a few strategies to ensure a more stable behaviour of the algorithm in this game genre.
Original languageEnglish
Title of host publicationProceedings of the International Conference on the Foundations of Digital Games
PublisherAssociation for Computing Machinery
Publication date2020
ISBN (Electronic)9781450388078
DOIs
Publication statusPublished - 2020
EventFoundations of Digitale Games - Malta, Malta
Duration: 16 Sep 202018 Sep 2020
Conference number: 2020

Conference

ConferenceFoundations of Digitale Games
Number2020
LocationMalta
Country/TerritoryMalta
Period16/09/202018/09/2020

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