论文标题
音乐中的在线游戏水平生成
Online Game Level Generation from Music
论文作者
论文摘要
游戏由多种类型的内容组成，而不同内容类型的和谐在游戏设计中起着至关重要的作用。但是，大多数关于程序内容生成的作品一次仅考虑一种类型的内容。在本文中，我们通过音乐匹配并实时地匹配音乐功能，同时适应玩家的比赛速度，从而提出并制定了从音乐中的在线水平发电。一个通用框架通过强化学习为在线玩家自适应的程序内容生成，oparl for Short是建立在经验驱动的强化学习和可控制的强化学习的基础上的，以从音乐中启用在线级别的生成。此外，提出了基于本地搜索和K-Nearest邻居的新型控制策略，并集成到Oparl中，以控制在线收集的播放数据的级别发电机。基于仿真的实验的结果表明，我们实施Oparl的实施有能力在在线方式以``能量''动态的``能量''动态来生成可玩水平。
Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the ``energy'' dynamic of music for different artificial players in an online fashion.
