|Autors: YOSHIKAWA,M., KIHIRA,T., TERAI,H.|
Title: Genetic learning using adaptive action value tables
Keywords: Q-Learning, Genetic Algorithm ,Sharing knowledge, Adaptive action value table ,Hierarchical evolutionary mechanism
Abstract: Reinforcement learning is applied to various fields such as robotics and mechatronics control.The reinforcement learning is an efficient method to control in unknown environment.This paper discusses a new reinforcement learning algorithm using adaptive action value tables.The proposed learning algorithm is based on Genetic Algorithm and enables sharing knowledge among agents.Regarding sharing knowledge,it introduces hierarchical evolutionary mechanism and the knowledge is inherited in one generation.As a result,the proposed algorithm achieves not only effective learning but also robustness.Experiments using pong simulator prove the effectiveness of the proposed algorithm.
Вид: публикация в национален форум