  To accomplish complex swarm robotic missions in the real world, one needs to
plan and execute a combination of single robot behaviors, group primitives such
as task allocation, path planning, and formation control, and mission-specific
objectives such as target search and group coverage. Most such missions are
designed manually by teams of robotics experts. Recent work in automated
approaches to learning swarm behavior has been limited to individual primitives
with sparse work on learning complete missions. This paper presents a
systematic approach to learn tactical mission-specific policies that compose
primitives in a swarm to accomplish the mission efficiently using neural
networks with special input and output encoding. To learn swarm tactics in an
adversarial environment, we employ a combination of 1) map-to-graph
abstraction, 2) input/output encoding via Pareto filtering of points of
interest and clustering of robots, and 3) learning via neuroevolution and
policy gradient approaches. We illustrate this combination as critical to
providing tractable learning, especially given the computational cost of
simulating swarm missions of this scale and complexity. Successful mission
completion outcomes are demonstrated with up to 60 robots. In addition, a close
match in the performance statistics in training and testing scenarios shows the
potential generalizability of the proposed framework.
