Publications:Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors
From ISLAB/CAISR
| Title | Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors |
|---|---|
| Author | Eric A. Antonelo and Albert-Jan Baerveldt and Thorsteinn Rögnvaldsson and Mauricio Figueiredo |
| 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. |
