Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization

• URL: http://arxiv.org/abs/2403.19462v1
• Date: Thu, 28 Mar 2024 14:34:02 GMT
• Title: Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization
• Authors: Teodor V. Marinov, Alekh Agarwal, Mircea Trofin,
• Abstract summary: We study a Reinforcement Learning problem in which we are given a set of trajectories collected with K baseline policies. The goal is to learn a policy which performs as well as the best combination of baselines on the entire state space.
• Score: 17.729842629392742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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