  Cooperative multi-agent reinforcement learning is a powerful tool to solve
many real-world cooperative tasks, but restrictions of real-world applications
may require training the agents in a fully decentralized manner. Due to the
lack of information about other agents, it is challenging to derive algorithms
that can converge to the optimal joint policy in a fully decentralized setting.
Thus, this research area has not been thoroughly studied. In this paper, we
seek to systematically review the fully decentralized methods in two settings:
maximizing a shared reward of all agents and maximizing the sum of individual
rewards of all agents, and discuss open questions and future research
directions.

Multiagent Systems;Artificial Intelligence;Machine Learning