  In order to avoid conventional controlling methods which created obstacles
due to the complexity of systems and intense demand on data density, developing
modern and more efficient control methods are required. In this way,
reinforcement learning off-policy and model-free algorithms help to avoid
working with complex models. In terms of speed and accuracy, they become
prominent methods because the algorithms use their past experience to learn the
optimal policies. In this study, three reinforcement learning algorithms; DDPG,
TD3 and SAC have been used to train Fetch robotic manipulator for four
different tasks in MuJoCo simulation environment. All of these algorithms are
off-policy and able to achieve their desired target by optimizing both policy
and value functions. In the current study, the efficiency and the speed of
these three algorithms are analyzed in a controlled environment.
