We provide open, transparent implementation and assessment of Google Brain's deep reinforcement learning approach to macro placement and its Circuit Training (CT) implementation in GitHub. We implement in open source key "blackbox" elements of CT, and clarify discrepancies between CT and Nature paper. New testcases on open enablements are developed and released. We assess CT alongside multiple alternative macro placers, with all evaluation flows and related scripts public in GitHub. Our experiments also encompass academic mixed-size placement benchmarks, as well as ablation and stability studies. We comment on the impact of Nature and CT, as well as directions for future research.

翻译：我们提供了谷歌大脑深度强化学习方法在宏布局及其GitHub中Circuit Training（CT）实现的开源、透明实现与评估。我们以开源方式实现了CT中的关键“黑箱”要素，并澄清了CT与《自然》论文之间的差异。针对开源工艺设计，我们开发并发布了新的测试案例。我们结合多种替代性宏布局工具对CT进行了评估，所有评估流程及相关脚本均已在GitHub公开。实验还涵盖了学术界的混合尺寸布局基准测试，以及消融研究与稳定性分析。我们探讨了《自然》论文与CT的影响，并提出了未来研究方向。