Selective exploration exploiting skills in hierarchical reinforcement learning framework
Gakuto Masuyama, Atsushi Yamashita, Hajime Asama · 2013
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.