  Reinforcement learning (RL) has recently achieved tremendous successes in
many artificial intelligence applications. Many of the forefront applications
of RL involve multiple agents, e.g., playing chess and Go games, autonomous
driving, and robotics. Unfortunately, the framework upon which classical RL
builds is inappropriate for multi-agent learning, as it assumes an agent's
environment is stationary and does not take into account the adaptivity of
other agents. In this review paper, we present the model of stochastic games
for multi-agent learning in dynamic environments. We focus on the development
of simple and independent learning dynamics for stochastic games: each agent is
myopic and chooses best-response type actions to other agents' strategy without
any coordination with her opponent. There has been limited progress on
developing convergent best-response type independent learning dynamics for
stochastic games. We present our recently proposed simple and independent
learning dynamics that guarantee convergence in zero-sum stochastic games,
together with a review of other contemporaneous algorithms for dynamic
multi-agent learning in this setting. Along the way, we also reexamine some
classical results from both the game theory and RL literature, to situate both
the conceptual contributions of our independent learning dynamics, and the
mathematical novelties of our analysis. We hope this review paper serves as an
impetus for the resurgence of studying independent and natural learning
dynamics in game theory, for the more challenging settings with a dynamic
environment.

Computer Science and Game Theory;Machine Learning;Dynamical Systems