  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.

Computer Science and Game Theory