  Deep reinforcement learning (DRL) is a promising approach for adaptive robot
control, but its current application to robotics is currently hindered by high
sample requirements. We propose two novel data augmentation techniques for DRL
in order to reuse more efficiently observed data. The first one called
Kaleidoscope Experience Replay exploits reflectional symmetries, while the
second called Goal-augmented Experience Replay takes advantage of lax goal
definitions. Our preliminary experimental results show a large increase in
learning speed.
