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A L-MCRS dynamics approximation by ELM for Reinforcement Learning

  • Published:2015-02 Issue: Volume:150 Page:116-123
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

Lopez-Guede Jose Manuel,Fernandez-Gauna Borja,Ramos-Hernanz Jose Antonio

Funder

Research Vicerectorship

Basque Government

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference29 articles.

1. On the potential contributions of hybrid intelligent approaches to multicomponent robotic system development;Duro;Inf. Sci.,2010

2. Linked multi-component mobile robots;Echegoyen;Robot. Autonom. Syst.,2010

3. Learning hose transport control with q-learning;Fernandez-Gauna;Neural Netw. World,2010

4. Towards concurrent q-learning on linked multi-component robotic systems;Fernandez-Gauna,2011

5. Empirical study of q-learning based elemental hose transport control;Lopez-Guede,2011

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1. A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines;2020 International Joint Conference on Neural Networks (IJCNN);2020-07

2. Extreme Trust Region Policy Optimization for Active Object Recognition;IEEE Transactions on Neural Networks and Learning Systems;2018-06

3. Experiments of conditioned reinforcement learning in continuous space control tasks;Neurocomputing;2018-01

4. A Short Review of Recent ELM Applications;Communications in Computer and Information Science;2018

5. Active object recognition using hierarchical local-receptive-field-based extreme learning machine;Memetic Computing;2017-05-10
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