AN ACTOR-CRITIC REINFORCEMENT LEARNING ALGORITHM BASED ON ADAPTIVE RBF NETWORK

被引：0

作者：

Li, Chun-Gui ^{[1
]}

Wang, Meng ^{[1
]}

Huang, Zhen-Jin ^{[1
]}

Zhang, Zeng-Fang ^{[1
]}

机构：

[1] Guangxi Univ Technol, Dept Comp Engn, Liuzhou 545006, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Actor-Critic reinforcement learning; Exploration strategy; Function approximation; Adaptive RBF network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We introduce an algorithm of Actor-Critic reinforcement learning methods in continuous state space. In order to cope with large-scale or continuous state spaces, the algorithm utilizes applied radial basis function (RBF) neural network to approximate the state value function. By training self-adapted non-linear processing unit, realizing online adaptive reconstructing of state space, the approximation is improved. In order to improve the efficient of exploration, a hybrid exploration strategy is proposed. Experimental studies concerning a Mountain-Car control task illustrate the performance and applicability of the proposed algorithm.

引用

页码：984 / 988

页数：5

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