  Reinforcement Learning (RL) is a learning paradigm concerned with learning to
control a system so as to maximize an objective over the long term. This
approach to learning has received immense interest in recent times and success
manifests itself in the form of human-level performance on games like
\textit{Go}. While RL is emerging as a practical component in real-life
systems, most successes have been in Single Agent domains. This report will
instead specifically focus on challenges that are unique to Multi-Agent Systems
interacting in mixed cooperative and competitive environments. The report
concludes with advances in the paradigm of training Multi-Agent Systems called
\textit{Decentralized Actor, Centralized Critic}, based on an extension of MDPs
called \textit{Decentralized Partially Observable MDP}s, which has seen a
renewed interest lately.
