  Following the remarkable success of the AlphaGO series, 2019 was a booming
year that witnessed significant advances in multi-agent reinforcement learning
(MARL) techniques. MARL corresponds to the learning problem in a multi-agent
system in which multiple agents learn simultaneously. It is an
interdisciplinary domain with a long history that includes game theory, machine
learning, stochastic control, psychology, and optimisation. Although MARL has
achieved considerable empirical success in solving real-world games, there is a
lack of a self-contained overview in the literature that elaborates the game
theoretical foundations of modern MARL methods and summarises the recent
advances. In fact, the majority of existing surveys are outdated and do not
fully cover the recent developments since 2010. In this work, we provide a
monograph on MARL that covers both the fundamentals and the latest developments
in the research frontier. The goal of our monograph is to provide a
self-contained assessment of the current state-of-the-art MARL techniques from
a game theoretical perspective. We expect this work to serve as a stepping
stone for both new researchers who are about to enter this fast-growing domain
and existing domain experts who want to obtain a panoramic view and identify
new directions based on recent advances.

Multiagent Systems;Artificial Intelligence