Reinforcement Learning for a Visually-Guided Autonomous Underwater Vehicle

Abstract

Reinforcement learning uses a scalar reward signal and much interaction with the environment to form a poli correct behavior. We have applied this technique to the problem of developing a controller for an autonomou underwater vehicle and have achieved reliable off-line development of stable controllers. Many important underwater tasks rely upon on visual observation of underwater features. We have devised a ture tracking method and a vehicle guidance scheme that are also based on visual observation of features. W obtained results in reliably tracking features in underwater imagery, not for map building but to guide an unde ter vehicle. Using visual servo control techniques, feature position can be used directly to guide motion.