A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2005 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/2169 |
| Acceso en línea: | http://hdl.handle.net/10256/2169 |
| Access Level: | acceso abierto |
| Palabra clave: | Algorismes computacionals Aprenentatge per reforç Intel·ligència artificial Robots autònoms Xarxes neuronals (Informàtica) Vehicles submergibles Artificial intelligence Autonomous robots Computer algorithms Neural networks (Computer science) Reinforcement learning Submersibles |
| Sumario: | This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs |
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