Lemma 2.Suppose Assumptions 1 and 2 hold.

Underthisassumption,weintroduce IMED-RLandprove that its regret upper bound asymptotically matches the regret lower bound.

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.

Table 3 presents the details of datasets in HoK1v1 task. Spells set to frenzy . Generally, a level of "1" is used for datasets with the "norm" prefix, while a level This distinction indicates varying levels of difficulty. In the Generalization category, "norm_general" and "hard_general," have their corresponding datasets. For example, to sample the "norm_general" dataset, we let the level-1 model fight with level-0, level-542 For example, in the "norm_hero_general" experiment, we directly use the model trained on "norm_medium" dataset only contains the fixed default hero "luban."

We tackle this planning issue by extending the policy gradient theory to policy updates with respecttoanystatedensity.

Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative.