The Open Electrical & Electronic Engineering Journal

2016, 10 : 69-79
Published online 2016 July 29. DOI: 10.2174/1874129001610010069
Publisher ID: TOEEJ-10-69

RESEARCH ARTICLE
 Hierarchical Reinforcement Learning Based Self-balancing Algorithm for Two-wheeled Robots

Juan Yan, * and Huibin Yang

* Address correspondence to this author at the College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai, China; E-mail: yanjuanxz@sina.com

ABSTRACT

Self-balancing control is the basis for applications of two-wheeled robots. In order to improve the self-balancing of two-wheeled robots, we propose a hierarchical reinforcement learning algorithm for controlling the balance of two-wheeled robots. After describing the subgoals of hierarchical reinforcement learning, we extract features for subgoals, define a feature value vector and its corresponding weight vector, and propose a reward function with additional subgoal reward function. Finally, we give a hierarchical reinforcement learning algorithm for finding the optimal strategy. Simulation experiments show that, the proposed algorithm is more effectiveness than traditional reinforcement learning algorithm in convergent speed. So in our system, the robots can get self-balanced very quickly.

Keywords:

Balancing control, Hierarchical reinforcement learning, Q-learning, Robot.