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Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors

on Thu, 09/29/2016 - 19:51
TitleModular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
Publication TypeConference Paper
Year of Publication2006
AuthorsAntonelo EA, Baerlvedt A-J, Rognvaldsson T, Figueiredo M
Conference NameProceedings of the International Joint Conference on Neural Networks (IJCNN)
Date PublishedJul.
Conference LocationVancouver, BC
KeywordsESN
Abstract

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.