Parallel Q-learning for a block-pushing problem

This paper presents an application of reinforcement learning to a block-pushing problem. The manipulator system we used is able to push millimeter size objects on a glass slide under a CCD camera. The objective is to automate high level tasks of pushing. Our approach is based on reinforcement learning algorithm (Q-Learning) because the models of the manipulator and of the dynamics of objects are unknown. The system is too complex for a classic algorithm, so we propose an original architecture which realizes several learning processes at the same time. This method produces an almost optimal policy whatever the number of manipulated objects may be. Some simulations allowed us to optimize every parameter of the learning process. In particular, they show that the more objects there are, the faster the controller learns. The experimental tests show that, after the learning process, the controller fills his part perfectly.