  Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. In this review article, we have focused on presenting
recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. In
particular, we have focused on five common approaches on modeling and solving
cooperative multi-agent reinforcement learning problems: (I) independent
learners, (II) fully observable critic, (III) value function factorization,
(IV) consensus, and (IV) learn to communicate. First, we elaborate on each of
these methods, possible challenges, and how these challenges were mitigated in
the relevant papers. If applicable, we further make a connection among
different papers in each category. Next, we cover some new emerging research
areas in MARL along with the relevant recent papers. Due to the recent success
of MARL in real-world applications, we assign a section to provide a review of
these applications and corresponding articles.
  Also, a list of available environments for MARL research is provided in this
survey. Finally, the paper is concluded with proposals on the possible research
directions.
