Measuring Inflation within Virtual Economies using Deep Reinforcement Learning

Stephens, Conor; Exton, Chris

doi:10.5220/0010392804440453

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2024

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Cited by 6 publications

(2 citation statements)

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“…Specifically, we describe the details of these criteria in Section II-A. Furthermore, a previous study [30] proposed DRL agent play-testing in a collective meta MMORPG game. Here, the DRL agents involve two gameplay styles, i.e., craftsman and adventurer, to simulate the in-game economics and assess the inflation phenomenon.…”

Section: Related Workmentioning

confidence: 99%

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

Jeon,

Baek,

Bae

et al. 2024

IEEE Trans. Games

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Section: Related Workmentioning

confidence: 99%

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

Jeon,

Baek,

Bae

et al. 2024

IEEE Trans. Games

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“…Sources handle resource creation, drains their destruction, converters change resources into others, and traders swap resource ownerships. More recently, Stephens and Exton have explored inflation (and its mitigation) in virtual economies [14].…”

Section: Background and Related Workmentioning

confidence: 99%

Using Evolutionary Algorithms to Target Complexity Levels in Game Economies

et al. 2023

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Game economies (GEs) describe how resources in games are created, transformed, or exchanged: They underpin most games and exist in different complexities. Their complexity may directly impact player difficulty. Nevertheless, neither difficulty nor complexity adjustment has been explored for GEs. Moreover, there is a lack of knowledge about complexity in GEs, how to define or assess it, and how it can be employed by automated adjustment approaches in game development to target specific complexity. We present a proof-of-concept for using evolutionary algorithms to craft targeted complexity graphs to model GEs. In a technical evaluation, we tested our first working definition of complexity in GEs. We then evaluated player-perceived complexity in a city-building game prototype through a user study and confirmed the generated GEs' complexity in an online survey. Our approach toward reliably creating GEs of specific complexity can facilitate game development and player testing but also inform and ground research on player perception of GE complexity.

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Advances in Measuring Inflation Within Virtual Economies Using Deep Reinforcement Learning

Stephens

Exton²

2022

Lecture Notes in Computer Science

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Measuring Inflation within Virtual Economies using Deep Reinforcement Learning

Cited by 6 publications

References 0 publications

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

Using Evolutionary Algorithms to Target Complexity Levels in Game Economies

Advances in Measuring Inflation Within Virtual Economies Using Deep Reinforcement Learning

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