文献类型：期刊文献

英文题名：Hierarchical reinforcement learning based on KNN classification algorithms

作者：Zhu, Shanhong[1,2]; Dong, Weipeng[1]; Liu, Wei[2]

第一作者：Zhu, Shanhong;朱珊虹

机构：[1] School of Computer and Information Engineering, Xinxiang University, Henan, China; [2] InternationalSchool of Software, Wuhan University, Wuhan, China

第一机构：新乡学院计算机与信息工程学院

年份：2015

卷号：8

期号：8

起止页码：175-184

外文期刊名：International Journal of Hybrid Information Technology

收录：EI(收录号：20153701267211)

语种：英文

外文关键词：Algorithms - Artificial intelligence - Autonomous agents - Engineering education - Learning systems - Reinforcement learning

摘要：In recent years, machine learning is increasingly becoming an important field of computer science. A new method using KNN classification algorithm identifies the layered boundary to find subgoal condition, to automatic classifying of large state space, reaches the dimension reduction of state space, and on the basis of generated subspace classifying to structure subtasks, and then realizes the hierarchical learning tasks automatically. In autonomous system, Agent assigns to their task through interaction with the environment, using hierarchical reinforcement learning technology can help the Agent in the large, complex environment to improve learning efficiency. Through the experimental results the effectiveness of the proposed algorithm is demonstrated. The goal of this paper is to provide a basic overview for both specialists and non-specialists to how to decide a good reinforcement learning algorithm for classification. ? 2015, SERSC.