Robots in manufacturing are programmed to achieve specific tasks in a deterministic environment. However, we see recently new manufacturing needs where robot controlling is required to be flexible. It became necessary to develop technologies to enable robot programming in this context. The challenge is to enable robots to accomplish assigned tasks within non-deterministic environments, where positions of machinery, robots, and objects are not static. Our goal is to investigate the elementary manufacturing task of robot grasping. In particular, we are interested in vision-based bin picking techniques which consist of robot arm grasping random objects from a bin using visual inputs. In this work, we investigate the possibility to apply Deep Reinforcement Learning to accomplish this task in a simulated environment.