Hybrid coordination of reinforcement learning-based behaviors for AUV control

摘要

This paper proposes a Hybrid Coordination method for Behavior-based Control Architectures. The hybrid method takes in advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of this hybrid method with a 3D-navigation application to an Autonomous Underwater Vehicle (AUV). The behaviors were learnt online by means of Reinforcement Learning. Continuous Q-learning implemented with a feed-forward neural network was applied. Realistic simulations were carried out. Results show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors.