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DC Field | Value | Language |
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dc.contributor.author | Shu, Tianye | - |
dc.contributor.author | Liu, Jialin | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.date.accessioned | 2021-09-07T09:26:54Z | - |
dc.date.available | 2021-09-07T09:26:54Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Shu, T., Liu, J., & Yannakakis, G. N. (2021). Experience-driven PCG via reinforcement learning : a Super Mario Bros study. 3rd IEEE Conference on Games, Copenhagen. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/80812 | - |
dc.description | This work was supported by the National Natural Science Foundation of China (Grant No. 61906083), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515011830), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531) and the Shenzhen Fundamental Research Program (Grant No. JCYJ20190809121403553). | en_GB |
dc.description.abstract | We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster’s principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Level design (Computer science) | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Computational intelligence | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Super Mario Bros. (Game) | en_GB |
dc.title | Experience-driven PCG via reinforcement learning : a Super Mario Bros study | 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 | 3rd IEEE Conference on Games | en_GB |
dc.bibliographicCitation.conferenceplace | Copenhagen, Denmark, 17-20/08/2021 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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