  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-Meszaros problems. Code for RLQP is available at
https://github.com/berkeleyautomation/rlqp.
