Reference : Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navi...
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Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
Antonelo, Eric Aislan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Baerlvedt, Albert-Jan [> >]
Rognvaldsson, Thorsteinn [> >]
Figueiredo, Mauricio [> >]
The 2006 IEEE International Joint Conference on Neural Network Proceedings
Vancouver, BC
2006 International Joint Conference on Neural Networks (IJCNN)
16-07-2006 21-07-2006
[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.

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