  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.
