Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
  1.1 KNN Classificaton Analysis
  1.2. Q-learning
  1.3. Option
 2. Hierarchical Reinforcement Learning Method based on KNN Classification
  2.2. Different Parameter for KNN
 3. The Experimental Simulation and Analysis
 4. Conclusion
 Acknowledgments
 References

키워드

저자

• Shanhong Zhu [ School of Computer and Information Engineering, Xinxiang University, Henan, China, International School of Software, Wuhan University, Wuhan, China ]
• Weipeng Dong [ School of Computer and Information Engineering, Xinxiang University, Henan, China ]
• Wei Liu [ International School of Software, Wuhan University, Wuhan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605