发表机构
LG AI Research（LG人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在改进PCB布线，介绍基于KiCad EDA引擎的开源环境PCBWorld及数据集PCBWorld-Bench，支持多种智能体。实验表明其中智能体性能优于基线，仅在合成板训练的强化学习策略可零样本迁移到真实板，有望提升布线能力。
AI 中文摘要
PCB布线是在严格设计规则下用铜走线连接电路板网络的任务，但基于学习的方法仍落后于基于规则的路由器。我们介绍了PCBWorld，一个基于KiCad EDA引擎构建的开源基于引擎的PCB布线环境。如同人类工程师一样，PCBWorld中的智能体通过引擎的原生操作交互式地对电路板进行布线，并利用其设计规则检查（DRC）反馈使布线符合设计规则。该环境支持强化学习策略和使用工具的语言模型智能体。此外，PCBWorld-Bench提供了三个以KiCad原生电路板格式（.kicad_pcb）的数据集家族，涵盖两种可控的合成实例和679个真实开源电路板。它使用八个经引擎检查的评估指标对任何完成的电路板进行评分，而不考虑布线方法。在我们的实验中，PCBWorld中的智能体始终优于网格动作强化学习策略和开环语言模型基线，并且仅在合成电路板上训练的强化学习策略能够零样本迁移到真实电路板上，接近基于规则的路由器。这些结果表明，基于引擎的交互式方法PCBWorld是提升强化学习和语言模型智能体布线能力的有前途的基础。
英文摘要
PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers. We introduce PCBWorld, an open-source engine-grounded PCB routing environment built on KiCad, an electronic design automation (EDA) engine. As a human engineer does, agents in PCBWorld interactively route a board through the engine's native operations, guided by its Design Rule Check (DRC) feedback. The environment supports both RL and tool-using LLM agents. Alongside the environment, PCBWorld-Bench provides three board datasets in the native .kicad_pcb format, two controllable synthetic generators and 679 real open-source boards. It scores any completed board with eight engine-checked evaluation metrics, regardless of the routing method. In our experiments, agents in PCBWorld consistently outperformed grid-action RL policies and open-loop LLM baselines, and an RL policy trained only on synthetic boards transferred zero-shot to real boards, approaching rule-based routers. PCBWorld and PCBWorld-Bench are available at https://github.com/LGAI-Research/PCBWorld.
CommentsAccepted to the KDD 2026 Workshop on Evaluation and Trustworthiness of Agentic AI (non-archival). Main text with appendix
