Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies is too small for these methods to be used in commercial EDA tools. We introduce the XRoute Environment, a new reinforcement learning environment where agents are trained to select and route nets in an advanced, end-to-end routing framework. Novel algorithms and ideas can be quickly tested in a safe and reproducible manner in it. The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license. In addition, it provides support for distributed deployment and multi-instance experiments. We propose two tasks for learning and build a full-chip test bed with routing benchmarks of various region sizes. We also pre-define several static routing regions with different pin density and number of nets for easier learning and testing. For net ordering task, we report baseline results for two widely used reinforcement learning algorithms (PPO and DQN) and one searching-based algorithm (TritonRoute). The XRoute Environment will be available at https://github.com/xplanlab/xroute_env.

翻译：布线是现代先进技术节点设计自动化流程中一个关键且耗时的阶段。强化学习领域的重大进展使得利用这些方法提升布线质量和效率成为可能。然而，近期研究中基于强化学习方法所解决的布线问题规模过小，难以应用于商业电子设计自动化工具。我们提出了XRoute环境——一种新型的强化学习环境，在该环境中，智能体可在一个先进的端到端布线框架内训练以选择并连接线网。新颖的算法和思路可在该环境中进行安全、可重复的快速测试。该环境具有挑战性、易于使用、可定制且支持添加额外场景，并以宽松的开源许可协议提供。此外，它还支持分布式部署和多实例实验。我们设计了两项学习任务，并构建了一个包含不同区域规模布线基准的全芯片测试平台。同时，为便于学习与测试，我们预定义了多个静态布线区域，这些区域具有不同的引脚密度和线网数量。针对线网排序任务，我们报告了两种广泛使用的强化学习算法（PPO和DQN）及一种基于搜索的算法（TritonRoute）的基线结果。XRoute环境将在https://github.com/xplanlab/xroute_env上开放获取。