  Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achieve a global objective can be challenging. Although it is
often possible to design an optimal centralized strategy for smaller numbers of
agents, those methods can fail as the number of agents increases. Motivated by
the growing success of machine learning, we develop a deep learning approach
that learns distributed coordination policies from centralized policies. In
contrast to traditional distributed control approaches, which are usually based
on human-designed policies for relatively simple tasks, this learning-based
approach can be adapted to more difficult tasks. We demonstrate the efficacy of
our proposed approach on two different tasks, the well-known rendezvous problem
and a more difficult particle assignment problem. For the latter, no known
distributed policy exists. From extensive simulations, it is shown that the
performance of the learned coordination policies is comparable to the
centralized policies, surpassing state-of-the-art distributed policies.
Thereby, our proposed approach provides a promising alternative for real-world
coordination problems that would be otherwise computationally expensive to
solve or intangible to explore.
