  In fully cooperative multi-agent reinforcement learning (MARL) settings,
environments are highly stochastic due to the partial observability of each
agent and the continuously changing policies of other agents. To address the
above issues, we proposed a unified framework, called DFAC, for integrating
distributional RL with value function factorization methods. This framework
generalizes expected value function factorization methods to enable the
factorization of return distributions. To validate DFAC, we first demonstrate
its ability to factorize the value functions of a simple matrix game with
stochastic rewards. Then, we perform experiments on all Super Hard maps of the
StarCraft Multi-Agent Challenge and six self-designed Ultra Hard maps, showing
that DFAC is able to outperform a number of baselines.
