图 书 馆
合奏蒙特卡罗计划:一个实证研究Ensemble Monte-Carlo Planning: An Empirical Study |
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| 课程网址: | http://videolectures.net/icaps2011_fern_ensemble/ |
| 主讲教师: | Alan Fern |
| 开课单位: | 俄勒冈州立大学 |
| 开课时间: | 2011-07-21 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and single core models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model. |
| 关 键 词: | 蒙特卡洛计划算法; 搜索树; 单核模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-16：lxf |
| 阅读次数: | 52 |