  Collective behavior is widespread across the animal kingdom. To date,
however, the developmental and mechanistic foundations of collective behavior
have not been formally established. What learning mechanisms drive the
development of collective behavior in newborn animals? Here, we used deep
reinforcement learning and curiosity-driven learning -- two learning mechanisms
deeply rooted in psychological and neuroscientific research -- to build newborn
artificial agents that develop collective behavior. Like newborn animals, our
agents learn collective behavior from raw sensory inputs in naturalistic
environments. Our agents also learn collective behavior without external
rewards, using only intrinsic motivation (curiosity) to drive learning.
Specifically, when we raise our artificial agents in natural visual
environments with groupmates, the agents spontaneously develop ego-motion,
object recognition, and a preference for groupmates, rapidly learning all of
the core skills required for collective behavior. This work bridges the divide
between high-dimensional sensory inputs and collective action, resulting in a
pixels-to-actions model of collective animal behavior. More generally, we show
that two generic learning mechanisms -- deep reinforcement learning and
curiosity-driven learning -- are sufficient to learn collective behavior from
unsupervised natural experience.
