  Reinforcement learning has shown great promise in robotics thanks to its
ability to develop efficient robotic control procedures through self-training.
In particular, reinforcement learning has been successfully applied to solving
the reaching task with robotic arms. In this paper, we define a robust,
reproducible and systematic experimental procedure to compare the performance
of various model-free algorithms at solving this task. The policies are trained
in simulation and are then transferred to a physical robotic manipulator. It is
shown that augmenting the reward signal with the Hindsight Experience Replay
exploration technique increases the average return of off-policy agents between
7 and 9 folds when the target position is initialised randomly at the beginning
of each episode.
