• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Intelligence and AI in Games, IEEE Transactions on >Discovering Multimodal Behavior in Ms. Pac-Man Through Evolution of Modular Neural Networks
【24h】

Discovering Multimodal Behavior in Ms. Pac-Man Through Evolution of Modular Neural Networks

机译：通过模块化神经网络的进化发现豆女士的多峰行为

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multiobjective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e., multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called module mutation. Several versions of module mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.
机译：“ Pac-Man女士”是一款具有挑战性的视频游戏，其中需要多种行为方式：“ Pac-Man女士”必须在威胁中躲避幽灵，并在食用时捕获它们，此外还要在每个级别吃掉所有药丸。 ac-Pac-Man女士过去的学习行为方法已将游戏视为要使用整体策略表示学习的一项任务。相反，本文使用称为模块化多目标NEAT（MM-NEAT）的框架来发展模块化神经网络。每个模块定义一个单独的行为。根据可以是人为设计（即多任务）或通过演进自动发现的策略，在不同时间使用这些模块。可以使用称为模块突变的遗传算子来固定或发现适当数量的模块。本文评估了模块突变的几种版本。固定模块化网络和模块变更网络均胜过单片网络和多任务网络。有趣的是，最好的网络将模块专门用于关键行为（例如在诱使鬼魂靠近药丸后被包围时逃逸），这些行为不遵循游戏的常规划分来追逐可食用和逃逸的威胁鬼魂。结果表明，MM-NEAT可以发现具有挑战性的游戏中代理商的有趣且有效的行为。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号