  Cooperation in multi-agent learning (MAL) is a topic at the intersection of
numerous disciplines, including game theory, economics, social sciences, and
evolutionary biology. Research in this area aims to understand both how agents
can coordinate effectively when goals are aligned and how they may cooperate in
settings where gains from working together are possible but possibilities for
conflict abound. In this paper we provide an overview of the fundamental
concepts, problem settings and algorithms of multi-agent learning. This
encompasses reinforcement learning, multi-agent sequential decision-making,
challenges associated with multi-agent cooperation, and a comprehensive review
of recent progress, along with an evaluation of relevant metrics. Finally we
discuss open challenges in the field with the aim of inspiring new avenues for
research.
