Offline Imitation Learning from Multiple Baselines with Applications to
  Compiler Optimization
Teodor V. Marinov Alekh Agarwal Mircea Trofin
This work studies a Reinforcement Learning (RL) problem in which we are given
a set of trajectories collected with K baseline policies. Each of these
policies can be quite suboptimal in isolation, and have strong performance in
complementary parts of the state space. The goal is to learn a policy which
performs as well as the best combination of baselines on the entire state
space. We propose a simple imitation learning based algorithm, show a sample
complexity bound on its accuracy and prove that the the algorithm is minimax
optimal by showing a matching lower bound. Further, we apply the algorithm in
the setting of machine learning guided compiler optimization to learn policies
for inlining programs with the objective of creating a small binary. We
demonstrate that we can learn a policy that outperforms an initial policy
learned via standard RL through a few iterations of our approach.