Difference between revisions of "Publications:Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors"
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{{PublicationSetupTemplate|Author=Eric A. Antonelo, Albert-Jan Baerveldt, Thorsteinn Rögnvaldsson, Mauricio Figueiredo | {{PublicationSetupTemplate|Author=Eric A. Antonelo, Albert-Jan Baerveldt, Thorsteinn Rögnvaldsson, Mauricio Figueiredo | ||
|PID=239330 | |PID=239330 | ||
| − | |Name=Antonelo, Eric A. (Electronics and Information Systems (ELIS) department, Ghent university, Belgium);Baerveldt, Albert-Jan | + | |Name=Antonelo, Eric A. (Electronics and Information Systems (ELIS) department, Ghent university, Belgium);Baerveldt, Albert-Jan (albbaex) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Figueiredo, Mauricio ( State University of Maringá, Brazil) |
|Title=Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors | |Title=Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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| Title | Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors |
|---|---|
| Author | |
| Year | 2006 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | International Joint Conference on Neural Networks, 2006. IJCNN '06 |
| Conference | International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver |
| DOI | http://dx.doi.org/10.1109/IJCNN.2006.246723 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239330 |
| Abstract | 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. |
