  In reinforcement learning (RL), the term self-play describes a kind of
multi-agent learning (MAL) that deploys an algorithm against copies of itself
to test compatibility in various stochastic environments. As is typical in MAL,
the literature draws heavily from well-established concepts in classical game
theory and so this survey quickly reviews some fundamental concepts. In what
follows, we present a brief survey of self-play literature, its major themes,
criteria, and techniques, and then conclude with an assessment of current
shortfalls of the literature as well as suggestions for future directions.
