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Publications:Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors
Abstract <p>Classical reinforcement learning <p>Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.</p>between collisions and captures.</p>
Author Eric A. Antonelo + , Albert-Jan Baerveldt + , Thorsteinn Rögnvaldsson + , Mauricio Figueiredo +
Conference International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver
DOI http://dx.doi.org/10.1109/IJCNN.2006.246723  +
Diva http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239330
EndPage 505  +
HostPublication International Joint Conference on Neural Networks, 2006. IJCNN '06  +
PublicationType Conference Paper  +
Publisher IEEE Press  +
Series IEEE International Joint Conference on Neural Networks (IJCNN)  +
StartPage 498  +
Title Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors  +
Year 2006  +
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Categories Publication  +
Modification dateThis property is a special property in this wiki. 30 September 2016 20:41:04  +
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