Title :
Selective exploration exploiting skills in hierarchical reinforcement learning framework
Author :
Masuyama, Gakuto ; Yamashita, Atsushi ; Asama, Hajime
Author_Institution :
Dept. of Precision Mech., Chuo Univ., Tokyo, Japan
Abstract :
In this paper, novel reinforcement learning method with intrinsic motivation for reproducibility of the past successful experience is presented. The experience is extracted as skill, which is composed of action sequence and abstract knowledge about observed sensor input. Utilizing the collected skills, reproduction of the successful experience is attempted in novel and unknown environment. Consistent exploration and active reduction of search space are realized by learning with intrinsic motivation for reproducibility of experience. Simulation experiments in grid world demonstrate that proposed method significantly accelerate speed of learning.
Keywords :
learning (artificial intelligence); abstract knowledge; action sequence; experience reproducibility; grid world; hierarchical reinforcement learning framework; observed sensor input; search space reduction; selective exploration exploiting skills; successful experience reproduction; Abstracts; Learning (artificial intelligence); Mobile robots; Navigation; Robot sensing systems; Vectors;
Conference_Titel :
Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on
Conference_Location :
Tokyo
DOI :
10.1109/IROS.2013.6696426