  Imitation learning (IL) consists of a set of tools that leverage expert
demonstrations to quickly learn policies. However, if the expert is suboptimal,
IL can yield policies with inferior performance compared to reinforcement
learning (RL). In this paper, we aim to provide an algorithm that combines the
best aspects of RL and IL. We accomplish this by formulating several popular RL
and IL algorithms in a common mirror descent framework, showing that these
algorithms can be viewed as a variation on a single approach. We then propose
LOKI, a strategy for policy learning that first performs a small but random
number of IL iterations before switching to a policy gradient RL method. We
show that if the switching time is properly randomized, LOKI can learn to
outperform a suboptimal expert and converge faster than running policy gradient
from scratch. Finally, we evaluate the performance of LOKI experimentally in
several simulated environments.
