论文标题
一般视频游戏的滚动地平线整洁
Rolling Horizon NEAT for General Video Game Playing
论文作者
论文摘要
本文提出了一种新的统计前向计划（SFP）方法，即增强拓扑的滚动范围神经进化（RHNEAT）。与传统的滚动视野进化不同，即进化算法负责发展一系列动作，Rhneat实时进化了神经网络的权重和连接，在返回游戏中执行游戏之前，计划了前进的几步。在20个GVGAI游戏的集合中探索了不同版本的算法，并将其与其他SFP方法和最新结果进行了比较。尽管结果总体上不比其他SFP方法好，但适应不断变化的游戏功能的RHNEAT的性质已允许在其他方法传统上与之斗争的游戏中建立新的最新记录。此处提出的算法是一般的，并引入了一种在滚动范围进化技术中表示信息的新方法。
This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional Rolling Horizon Evolution, where an evolutionary algorithm is in charge of evolving a sequence of actions, rhNEAT evolves weights and connections of a neural network in real-time, planning several steps ahead before returning an action to execute in the game. Different versions of the algorithm are explored in a collection of 20 GVGAI games, and compared with other SFP methods and state of the art results. Although results are overall not better than other SFP methods, the nature of rhNEAT to adapt to changing game features has allowed to establish new state of the art records in games that other methods have traditionally struggled with. The algorithm proposed here is general and introduces a new way of representing information within rolling horizon evolution techniques.
