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Neural networks with online sequential learning ability for a reinforcement learning algorithm
Shah H.,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 27
Issue: VOL 1
Pages: 87 - 99
Reinforcement learning (RL) algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, neural network function approximators suffer from a number of problems like learning becomes difficult when the training data are given sequentially, difficult to determine structural parameters, and usually result in local minima or overfitting. In this paper, a novel on-line sequential learning evolving neural network model design for RL is proposed. We explore the use of minimal resource allocation neural network (mRAN), and develop a mRAN function approximation approach to RL systems. Potential of this approach is demonstrated through a case study. The mean square error accuracy, computational cost, and robustness properties of this scheme are compared with static structure neural networks. © Springer International Publishing Switzerland 2014.
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Published in Springer Science and Business Media Deutschland GmbH
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