Accelerating Quadratic Optimization with Reinforcement LearningDownload PDF

21 May 2021, 20:48 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: quadratic optimization, convex optimization, first-order methods, reinforcement learning for optimization
  • TL;DR: Using RL we train a policy to adapt internal parameters of a QP solver that allows the QP solver to converge faster
  • Abstract: First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes surprisingly well to previously unseen problems with varying dimension and structure from different applications, including the QPLIB, Netlib LP and Maros-M{\'e}sz{\'a}ros problems. Code, models, and videos are available at https://berkeleyautomation.github.io/rlqp/.
  • Supplementary Material: pdf
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  • Code: https://github.com/berkeleyautomation/rlqp
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