Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning. (arXiv:2206.00518v4 [cs.LG] UPDATED)

In deep reinforcement learning (RL), data augmentation is widely considered
as a tool to induce a set of useful priors about semantic consistency and
improve sample efficiency and generalization performance. However, even when
the prior is useful for generalization, distilling it to RL agent often
interferes with RL training and degenerates sample efficiency. Meanwhile, the
agent is forgetful of the prior due to the non-stationary nature of RL. These
observations suggest two extreme schedules of distillation: (i) over the entire …

arxiv augmentation data reinforcement reinforcement learning scheduling