  When researching robot swarms, many studies observe complex group behavior
emerging from the individual agents' simple local actions. However, the task of
learning an individual policy to produce a desired emergent behavior remains a
challenging and largely unsolved problem. We present a method of training
distributed robotic swarm algorithms to produce emergent behavior. Inspired by
the biological evolution of emergent behavior in animals, we use an
evolutionary algorithm to train a 'population' of individual behaviors to
approximate a desired group behavior. We perform experiments using simulations
of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics
platforms conducted in the CoppeliaSim simulator. Additionally, we test on
simulations of Anki Vector robots to display our algorithm's effectiveness on
various modes of actuation. We evaluate our algorithm on various tasks where a
somewhat complex group behavior is required for success. These tasks include an
Area Coverage task, a Surround Target task, and a Wall Climb task. We compare
behaviors evolved using our algorithm against 'designed policies', which we
create in order to exhibit the emergent behaviors we desire.
