Two-Stage Reinforcement Learning on Credit Branch Genetic Network Programming for Mobile Robots

スポンサーリンク

概要

• 論文の詳細を見る

• This paper proposes Two-Stage Reinforcement Learning on Credit Branch Genetic Network Programming named GNP-TSRL-CB for mobile robots. The proposed method uses 2 kinds of Q-tables for sub node selection and credit branch selection, which has advantages of (1) determining an alternative function by using sub node selection and (2) skipping useless functions by using credit branch selection. It is clarified from simulation results that the adaptability mechanism of the proposed method can improve the performance compared with the conventional methods when the individuals of GNP-TSRL-CB are implemented in the dynamic environments like the sudden changes occur.

著者

• Mabu Shingo

  The Graduate School Of Information Production And Systems Waseda Univ.

• Hirasawa Kotaro

  The Graduate School Of Information Production And Systems Waseda University

• Sendari Siti

  The State University of Malang

• Mabu Shingo

  The Graduate School of Information, Production, and Systems, Waseda University

関連論文

• Genetic Network Programming-Sarsa with Multi-Subroutines for Trading Rules on Stock Markets
• Two-Stage Reinforcement Learning on Credit Branch Genetic Network Programming for Mobile Robots

スポンサーリンク