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Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

IEEE

Autor: Carreras Pérez, Marc
Batlle i Grabulosa, Joan
Ridao Rodríguez, Pere
Resum: This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors
Accés al document: http://hdl.handle.net/2072/58623
Llenguatge: eng
Editor: IEEE
Drets: Tots els drets reservats
Matèria: Robots mòbils
Robots submarins
Vehicles submergibles
Mobile robots
Submersibles
Underwater robots
Títol: Hybrid coordination of reinforcement learning-based behaviors for AUV control
Tipus: info:eu-repo/semantics/article
Repositori: Recercat

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