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DC Field | Value | Language |
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dc.contributor.author | Khalifa, Ahmed | - |
dc.contributor.author | Bontrager, Philip | - |
dc.contributor.author | Earle, Sam | - |
dc.contributor.author | Togelius, Julian | - |
dc.date.accessioned | 2021-10-11T09:54:43Z | - |
dc.date.available | 2021-10-11T09:54:43Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Khalifa, A., Bontrager, P., Earle, S., & Julian, T. (2020). PCGRL : procedural content generation via reinforcement learning. Proceedings of the Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20), 16(1), 95-101. | en_GB |
dc.identifier.isbn | 9781577358497 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/81916 | - |
dc.description.abstract | We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes, and apply these to three game environments. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Level design (Computer science) | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.title | PCGRL : procedural content generation via reinforcement learning | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment | en_GB |
dc.bibliographicCitation.conferenceplace | Virtually, 19-23/10/2020 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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File | Description | Size | Format | |
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PCGRL_procedural_content_generation_via_reinforcement_learning_2020.pdf Restricted Access | 2.16 MB | Adobe PDF | View/Open Request a copy |
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