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Autonomous underwater vehicle control using reinforcement learning policy search methods
El-Fakdi Sencianes, Andrés; Carreras Pérez, Marc; Palomeras Rovira, Narcís; Ridao Rodríguez, Pere
Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task
2010-05-17
Aprenentatge per reforç
Robots autònoms -- Sistemes de control
Vehicles submergibles -- Sistemes de control
Autonomous robots -- Control systems
Reinforcement learning
Submersibles -- Control systems
Tots els drets reservats
Article
IEEE
         

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