Model-based and Model-free Reinforcement Learning for Visual Servoing
Amir massoud Farahmand, Azad Shademan, Martin Jagersand and Csaba Szepesvari
To address the difficulty of designing a controller
for complex visual-servoing tasks, two learning-based uncalibrated
approaches are introduced. The first method starts by
building an estimated model for the visual-motor forward kinematic
of the vision-robot system by a locally linear regression
method. Afterwards, it uses a reinforcement learning method
named Regularized Fitted Q-Iteration to find a controller (i.e.
policy) for the system (model-based RL). The second method
directly uses samples coming from the robot without building
any intermediate model (model-free RL). The simulation results
show that both methods perform comparably well despite not
having any a priori knowledge about the robot.