Go with the flow: reinforcement learning in turn-based battle video games

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dc.contributor.author Pagalyte, Elinga
dc.contributor.author Mancini, Maurizio
dc.contributor.author Climent, Laura
dc.date.accessioned 2021-04-27T11:28:14Z
dc.date.available 2021-04-27T11:28:14Z
dc.date.issued 2020-10
dc.identifier.citation Pagalyte, E., Mancini, M. and Climent, L. (2020) 'Go with the Flow: Reinforcement Learning in Turn-based Battle Video Games', Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020 Virtual Event Scotland UK, 19-23 October. doi: 10.1145/3383652.3423868 en
dc.identifier.startpage 1 en
dc.identifier.endpage 8 en
dc.identifier.isbn 978-1-4503-7586-3
dc.identifier.uri http://hdl.handle.net/10468/11225
dc.identifier.doi 10.1145/3383652.3423868 en
dc.description.abstract Game flow represents a state where the player is neither frustrated nor bored. In turn-based battle video games it can be achieved by Dynamic Difficulty Adjustment (DDA), whose research has begun rising over the last decade. This paper introduces an idea for incorporating DDA through the use of Reinforcement Learning (RL) to agents of turn-based battle video games. We design and implement an RL agent that shows, in a simple environment, the idea of how a game could achieve balance through adequate choices in actions depending on the player's level of skill. For achieving this purpose, we incorporated the design and implementation of state-action-reward-state-action (SARSA) algorithm to the agent of our implemented game. In addition, we added tracking of the on-going games and depending on the frequency of the player's repeated wins or losses, the rewards of the RL agent are modified. This modification of the rewards has an impact on the RL agent's actions, which involves an increase/decrease of the difficulty of the battle game. The evaluation performed shows that the idea of the paper is demonstrated, since players face personalized challenges that we believe are in range of game flow. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery, ACM en
dc.relation.uri https://dl.acm.org/doi/10.1145/3383652.3423868
dc.rights © 2020 Association for Computing Machinery. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). en
dc.subject DDA en
dc.subject Dynamic Difficulty Adjustment en
dc.subject Game Flow en
dc.subject Reinforcement Learning en
dc.subject RL en
dc.subject SARSA en
dc.subject Turn-based battle video game en
dc.title Go with the flow: reinforcement learning in turn-based battle video games en
dc.type Conference item en
dc.internal.authorcontactother Laura Climent, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: laura.climent@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2021-04-27T11:18:41Z
dc.description.version Accepted Version en
dc.internal.rssid 564018403
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Virtual Event, Scotland, UK en
dc.internal.IRISemailaddress laura.climent@cs.ucc.ie en
dc.internal.IRISemailaddress m.mancini@cs.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en

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