  Deep Reinforcement Learning (RL) is a promising approach for adaptive robot
control, but its current application to robotics is currently hindered by high
sample requirements. To alleviate this issue, we propose to exploit the
symmetries present in robotic tasks. Intuitively, symmetries from observed
trajectories define transformations that leave the space of feasible RL
trajectories invariant and can be used to generate new feasible trajectories,
which could be used for training. Based on this data augmentation idea, we
formulate a general framework, called Invariant Transform Experience Replay
that we present with two techniques: (i) Kaleidoscope Experience Replay
exploits reflectional symmetries and (ii) Goal-augmented Experience Replay
which takes advantage of lax goal definitions. In the Fetch tasks from OpenAI
Gym, our experimental results show significant increases in learning rates and
success rates. Particularly, we attain a 13, 3, and 5 times speedup in the
pushing, sliding, and pick-and-place tasks respectively in the multi-goal
setting. Performance gains are also observed in similar tasks with obstacles
and we successfully deployed a trained policy on a real Baxter robot. Our work
demonstrates that invariant transformations on RL trajectories are a promising
methodology to speed up learning in deep RL.
