Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading.

翻译：数百万贫民窟居民因内部道路基础设施不足而难以获得城市服务，贫民窟道路规划对城市可持续发展至关重要。现有重新分区或启发式方法要么耗时且无法泛化至不同贫民窟，要么在可达性和建设成本方面产生次优道路规划方案。本文提出一种基于深度强化学习的贫民窟自动道路布局方法。我们建立通用图模型以捕捉贫民窟的拓扑结构，并设计新型图神经网络用于规划道路选址。通过掩码策略优化，该模型能以最小建设成本生成连接贫民窟各区域的道路规划方案。在多个国家真实贫民窟上的大量实验验证了模型有效性，相比现有基线方法可显著提升14.3%的可达性。跨任务迁移研究表明，该模型能掌握简单场景中的道路规划技能并适应复杂场景，揭示了其应用于实际贫民窟改造的潜力。