An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent Settings

2012 ◽  
pp. 261-272
Author(s):  
Ioannis Lambrou ◽  
Vassilis Vassiliades ◽  
Chris Christodoulou
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Hierarchical Reinforcement Learning ◽  
Reinforcement Learning Algorithm

scholarly journals Hierarchical Reinforcement Learning Based Self-balancing Algorithm for Two-wheeled Robots

2016 ◽  
Vol 10 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Juan Yan ◽  
Huibin Yang
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Weight Vector ◽  
The Self ◽  
Simulation Experiments ◽  
Wheeled Robots ◽  
Reward Function ◽  
Hierarchical Reinforcement Learning ◽  
Balancing Control ◽  
Reinforcement Learning Algorithm

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.

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