  Centralized approaches for multi-robot coverage planning problems suffer from
the lack of scalability. Learning-based distributed algorithms provide a
scalable avenue in addition to bringing data-oriented feature generation
capabilities to the table, allowing integration with other learning-based
approaches. To this end, we present a learning-based, differentiable
distributed coverage planner (D2COPL A N) which scales efficiently in runtime
and number of agents compared to the expert algorithm, and performs on par with
the classical distributed algorithm. In addition, we show that D2COPlan can be
seamlessly combined with other learning methods to learn end-to-end, resulting
in a better solution than the individually trained modules, opening doors to
further research for tasks that remain elusive with classical methods.
