  Recent advances in GPU accelerated global and detail placement have reduced
the time to solution by an order of magnitude. This advancement allows us to
leverage data driven optimization (such as Reinforcement Learning) in an effort
to improve the final quality of placement results. In this work we augment
state-of-the-art, force-based global placement solvers with a reinforcement
learning agent trained to improve the final detail placed Half Perimeter Wire
Length (HPWL).
  We propose novel control schemes with either global or localized control of
the placement process. We then train reinforcement learning agents to use these
controls to guide placement to improved solutions. In both cases, the augmented
optimizer finds improved placement solutions.
  Our trained agents achieve an average 1% improvement in final detail place
HPWL across a range of academic benchmarks and more than 1% in global place
HPWL on real industry designs.
