  Recently, successful applications of reinforcement learning to chip placement
have emerged. Pretrained models are necessary to improve efficiency and
effectiveness. Currently, the weights of objective metrics (e.g., wirelength,
congestion, and timing) are fixed during pretraining. However, fixed-weighed
models cannot generate the diversity of placements required for engineers to
accommodate changing requirements as they arise. This paper proposes flexible
multiple-objective reinforcement learning (MORL) to support objective functions
with inference-time variable weights using just a single pretrained model. Our
macro placement results show that MORL can generate the Pareto frontier of
multiple objectives effectively.
