  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. The code and data are available at
https://github.com/tsinghua-fib-lab/road-planning-for-slums.
