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Authors: Conor Stephens 1 ; 2 and Chris Exton 1 ; 2

Affiliations: 1 Computer Systems and Information Science, University of Limerick, Ireland ; 2 Lero, Science Foundation Ireland Centre for Software Research, Ireland

Keyword(s): Reinforcement, Learning, Games Design, Economies, Multiplayer, Games.

Abstract: This paper proposes a framework for assessing economies within online multiplayer games without the need for extensive player testing and data collection. Players have identified numerous exploits in modern online games to further their collection of resources and items. A recent exploit within a game-economy would be in Animal Crossing New Horizons a multiplayer game released in 2020 which featured bugs that allowed users to generate infinite money (Sudario, 2020); this has impacted the player experience in multiple negative ways such as causing hyperinflation within the economy and scarcity of resources within the particular confines of any game. The framework proposed by this paper can aid game developers and designers when testing their game systems for potential exploits that could lead to issues within the larger game economies. Assessing game systems is possible by leveraging reinforcement learning agents to model player behaviour; this is shown and evaluated in a sample multi player game. This research is designed for game designers and developers to show how multi-agent reinforcement learning can help balance game economies. The project source code is open source and available at: https://github.com/Taikatou/economy research. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Stephens, C. and Exton, C. (2021). Measuring Inflation within Virtual Economies using Deep Reinforcement Learning. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 444-453. DOI: 10.5220/0010392804440453

@conference{icaart21,
author={Conor Stephens. and Chris Exton.},
title={Measuring Inflation within Virtual Economies using Deep Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={444-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010392804440453},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Measuring Inflation within Virtual Economies using Deep Reinforcement Learning
SN - 978-989-758-484-8
IS - 2184-433X
AU - Stephens, C.
AU - Exton, C.
PY - 2021
SP - 444
EP - 453
DO - 10.5220/0010392804440453
PB - SciTePress

- Science and Technology Publications, Lda.
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