Estimating Player Completion Rate in Mobile Puzzle Games Using Reinforcement Learning

Jeppe Theiss Kristensen, Arturo Valdivia, Paolo Burelli

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


In this work we investigate whether it is plausibleto use the performance of a reinforcement learning (RL) agentto estimate the difficulty measured as the player completion rateof different levels in the mobile puzzle game Lily’s Garden.For this purpose we train an RL agent and measure thenumber of moves required to complete a level. This is thencompared to the level completion rate of a large sample of realplayers.We find that the strongest predictor of player completion ratefor a level is the number of moves taken to complete a level of the∼5% best runs of the agent on a given level. A very interestingobservation is that, while in absolute terms, the agent is unable toreach human-level performance across all levels, the differencesin terms of behaviour between levels are highly correlated to thedifferences in human behaviour. Thus, despite performing sub-par, it is still possible to use the performance of the agent toestimate, and perhaps further model, player metrics
Original languageEnglish
Title of host publication2020 IEEE Conference on Games (CoG)
Publication date2020
ISBN (Print)978-1-7281-4534-1
ISBN (Electronic)978-1-7281-4533-4
Publication statusPublished - 2020
EventIEEE Conference on Games 2020 -
Duration: 24 Aug 202027 Nov 2020


ConferenceIEEE Conference on Games 2020
Internet address


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