title: A reward optimization method based on action subrewards in hierarchical reinforcement learning.
abstract: Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.
authors.affiliation: Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, Jiangsu 215123, China ; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
authors.forename: Yuchen
authors.identifier: 0000-0002-7743-3635
authors.initials: Y
authors.lastname: Fu
authors.affiliation: School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
authors.forename: Xionghong
authors.initials: X
authors.lastname: Ling
authors.affiliation: School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Cui
mesh_terms: D001185:Artificial Intelligence; D008962:Models, Theoretical