  Placement Optimization is an important problem in systems and chip design,
which consists of mapping the nodes of a graph onto a limited set of resources
to optimize for an objective, subject to constraints. In this paper, we start
by motivating reinforcement learning as a solution to the placement problem. We
then give an overview of what deep reinforcement learning is. We next formulate
the placement problem as a reinforcement learning problem and show how this
problem can be solved with policy gradient optimization. Finally, we describe
lessons we have learned from training deep reinforcement learning policies
across a variety of placement optimization problems.
