Extreme Trust Region Policy Optimization for Active Object Recognition

IEEE Transactions on Neural Networks and Learning Systems
Huaping LiuYupei Wu

Abstract

In this brief, we develop a deep reinforcement learning method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects. The method is realized using trust region policy optimization, in which the policy is realized by an extreme learning machine and, therefore, leads to efficient optimization algorithm. The experimental results on the publicly available data set show the advantages of the developed extreme trust region optimization method.

References

Oct 11, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Guang-Bin HuangRui Zhang
Feb 27, 2015·Nature·Volodymyr MnihDemis Hassabis
Oct 25, 2016·IEEE Transactions on Cybernetics·Huaping LiuDi Guo

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Citations

Sep 5, 2018·Neural Networks : the Official Journal of the International Neural Network Society·Henghui ZhuMichael E Hasselmo
Apr 11, 2018·Neural Networks : the Official Journal of the International Neural Network Society·Ahmed Hussain QureshiHiroshi Ishiguro

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