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Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
Antonelo, Eric Aislan; Baerlvedt, Albert-Jan; Rognvaldsson, Thorsteinn et al.
2006In The 2006 IEEE International Joint Conference on Neural Network Proceedings
Peer reviewed
 

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Abstract :
[en] Classical reinforcement learning mechanisms and a modular neural network are unified to conceive 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. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust 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.
Disciplines :
Computer science
Author, co-author :
Antonelo, Eric Aislan ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Baerlvedt, Albert-Jan
Rognvaldsson, Thorsteinn
Figueiredo, Mauricio
External co-authors :
yes
Language :
English
Title :
Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
Publication date :
2006
Event name :
2006 International Joint Conference on Neural Networks (IJCNN)
Event date :
16-07-2006 21-07-2006
Audience :
International
Main work title :
The 2006 IEEE International Joint Conference on Neural Network Proceedings
ISBN/EAN :
0-7803-9490-9
Pages :
498-505
Peer reviewed :
Peer reviewed
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since 29 August 2018

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