Distinct from traditional heuristic solvers, this paper on one hand proposes an RL-based model for mixed-size macro placement, which differs from existing learning-based placers that often consider the macro by coarse grid-based mask. While the standard cells are placed via gradient-based GPU acceleration. On the other hand, a one-shot conditional generative routing model, which is composed of a special-designed input-size-adapting generator and a bi-discriminator, is devised to perform one-shot routing to the pins within each net, and the order of nets to route is adaptively learned.

Learning With Opponent-Learning A wareness (LOLA) (Foerster et al. [2018a]) is a multi-agent reinforcement learning algorithm that typically learns reciprocity-based

We jointly train the learner agent's policy and learn

Extending programming by example to more complex input domains is a very useful task, as it opens the range of possible real-world applications.