Hierarchical Reinforcement Learning Based Self-balancing Algorithm for Two-wheeled Robots
Juan Yan*, Huibin Yang
Identifiers and Pagination:Year: 2016
First Page: 69
Last Page: 79
Publisher Id: TOEEJ-10-69
Article History:Received Date: 15/02/2016
Revision Received Date: 10/05/2016
Acceptance Date: 15/05/2016
Electronic publication date: 29/07/2016
Collection year: 2016
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
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.