An Improved Rolling Horizon Evolution Algorithm with Shift Buffer for General Game Playing
An Improved Rolling Horizon Evolution Algorithm with Shift Buffer for General Game Playing
复制标题
一种改进的用于一般游戏的带有移位缓冲区的滚动地平线进化算法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
E. Hauck
中科院分区：
文献类型：
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作者：
Bruno Santos;H. Bernardino;E. Hauck
通用游戏（GGP）是人工智能程序的设计，可以玩多个游戏。这里，最著名的GGP框架之一，通用电子游戏AI竞赛（GVGAI）框架，被用来为Atari 2600启发的游戏设计控制器。GVGAI文献的最新进展表明，滚动地平线进化算法（RHEA）与其他方法相比具有竞争力，这鼓励了该方法的使用和改进研究。本文提出了在新个体创建过程中使用1- step -ahead方法和冗余动作避免策略。1步预瞄技术通过位移缓冲增强（RHEA- sb）提高了个体在移动后的动作选择，冗余动作回避策略降低了个体内部空间冗余动作的机会。此外，本文还对RHEA-SB进行了参数分析，其中评估了种群大小、模拟深度和种群中保留的个体数量的不同值。结果表明，与原算法相比，使用1步预判和冗余动作避免策略提高了解的质量。
General Game Playing (GGP) is the design of artificial intelligence programs to play more than one game. Here, one of the most famous GGP frameworks, The General Video Game AI Competition (GVGAI) Framework, is used in order to design controllers for Atari 2600 inspired games. Recent advancements in the literature of GVGAI showed that the Rolling Horizon Evolution Algorithm (RHEA) is competitive when compared to other methods, encouraging the use and the research by improvements for this method. The use of a 1-Step-Look-ahead approach and a Redundant Action Avoidance policy during the creation of new individuals are proposed in this paper. The 1-step-look-ahead technique improves the action selection after the shift of the individual in RHEA with the shift buffer enhancement (RHEA-SB), and the redundant action avoidance policy decreases the chance of spatial redundant actions within the individual. Also, a parameter analysis of RHEA-SB is performed here, where different values of population size, depth of simulations, and number of individuals that remains in the population are evaluated. Results show that using 1-Step-Look-ahead and a redundant action avoidance policy improves the quality of the solutions found when compared to the original algorithm.