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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/142638
    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/142638


    題名: 透過強化學習設計合作遊戲的夥伴
    Designing Game Companions in Cooperative Games Using Reinforcement Learning
    作者: 吳宥衡
    Wu, You-Heng
    貢獻者: 李蔡彥
    Li, Tsai-Yen
    吳宥衡
    Wu, You-Heng
    關鍵詞: 人工智慧
    強化學習
    非玩家角色
    遊戲夥伴
    Artificial Intelligent
    Reinforcement learning
    Non-player character
    Game companions
    日期: 2022
    上傳時間: 2022-12-02 15:19:54 (UTC+8)
    摘要: 電玩遊戲中與玩家互動的非玩家角色(Non-Player Character, NPC)一直是影響玩家遊戲體驗的要素,如何設計一個行為自然又能讓遊戲更加好玩的NPC遊戲夥伴不僅是遊戲業者一直以來努力的方向,也是許多玩家長年來的期待。本研究整理過去設計遊戲夥伴的相關文獻,探討玩家在玩遊戲過程中覺得好玩的原因,以及對遊戲夥伴的期待,發現大多數的玩家期望遊戲夥伴能觀察環境變化,並與玩家相互依賴合作。因此我們於Unity3D遊戲引擎設計一款雙人合作射擊遊戲,有別於過往使用強化學習設計完美通關遊戲的AI,本研究採用ML-Agents套件中近端策略優化(PPO)強化學習演算法的方式,一步一步讓遊戲夥伴學會新的遊戲技術,最後引導遊戲夥伴學會與玩家合作通關遊戲。本研究實驗請20名受試者分別與合作版本與非合作版本的遊戲夥伴一同闖關,透過受試者在實驗後給予的回饋,實驗結果也顯示了大多數玩家認為若遊戲夥伴能在遊戲過程中關注自身的狀態,並且在雙方有難時互相合作,可以更加有助於遊戲的正向體驗。
    The non-player character (NPC) that interacts with players in video games has al-ways been an element that affects the players` game experience. How to design an NPC game companion that behaves naturally and makes the games more interesting is not only what the game designers’ striving for, but also the expectation of many players for a long time. This study tries to figure out the reasons why players feel interested in playing games and their expectations of game companions. We have found that most players look forward to game companions to observe changes in the environment and to rely on and cooperate with players. Therefore, we designed a two-player cooperative shooting game in the Unity3D game engine. Differing from using traditional reinforce-ment learning to design game agents in the past, we use proximal policy optimization (PPO) algorithm with ML-Agents toolkit to design our game companions. We try to make game companions learn game skills step by step, and finally learn how to cooper-ate with players to clear the game. We invited twenty participants to participate in our experiment. The participants were asked to play our shooting games in the cooperative version and the non-cooperative version with game companions, respectively. Through the feedback given by the participants, the experimental results show that most players believe that if the game companions can pay attention to players’ state during the games, and cooperate with each other in trouble, it will contribute to more positive playing ex-periences of the game.
    參考文獻: [1] E. Bouquet, V. Mäkelä, and A. Schmidt, "Exploring the Design of Companions in Video Games," in Academic Mindtrek 2021, 2021, pp. 145-153.
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    [6] J. Chen, "Flow in games (and everything else)," Communications of the ACM, vol. 50, no. 4, pp. 31-34, 2007.
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    [14] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
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    [16] W. IJsselsteijn et al., "Measuring the Experience of Digital Game Enjoyment," in Proceedings of measuring behavior Conference, 2008, Noldus Maastricht, the Netherlands, pp. 88-89.
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    [18] R. Likert, "A technique for the measurement of attitudes," Archives of psychology, 1932.
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    描述: 碩士
    國立政治大學
    資訊科學系
    108753123
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108753123
    資料類型: thesis
    DOI: 10.6814/NCCU202201687
    顯示於類別:[資訊科學系] 學位論文

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