文章基本信息

标题：Continuous-Domain Reinforcement Learning Using a Self-Organizing Neural Network
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作者：Najmeh Alibabaie ; Mir Mohsen Pedram
期刊名称：International Journal of Mechatronics, Electrical and Computer Technology
印刷版ISSN：2305-0543
出版年度：2015
卷号：5
期号：14
页码：1999-2014
出版社：Austrian E-Journals of Universal Scientific Organization
摘要：This paper deals with a new algorithm for solving the problems of reinforcement learning in continuous spaces. For continuous states and actions, we use a new method based on self- organizing neural network, DIGNET. Two self-organizing neural network, DIGNETs, are used in this method, which having simple structure, can give an appropriate approximate of state/action space. The network is able to adapt inconsistent nature of reinforcement learning environments fine since the system parameters in DIGNET are self-adjusted in an autonomous way during the learning procedure. Automatic and competitive production and elimination of attraction wells, considering parameters of attraction well, threshold, age and depth, lead to flexibility of proposed algorithm to solve continuous problems and finally, the attraction wells of output DIGNET (action) will concentrate on the areas with high reward, providing an appropriate illustration for a continuous state space. At the end, we also present the results of evaluating proposed method on several problems
关键词：Machine Learning; Reinforcement Learning; Generalization; Q-learning; DIGNET