MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning. (arXiv:2202.10134v2 [cs.LG] UPDATED)

In cooperative multi-agent tasks, a team of agents jointly interact with an
environment by taking actions, receiving a team reward and observing the next
state. During the interactions, the uncertainty of environment and reward will
inevitably induce stochasticity in the long-term returns and the randomness can
be exacerbated with the increasing number of agents. However, such randomness
is ignored by most of the existing value-based multi-agent reinforcement
learning (MARL) methods, which only model the expectation of Q-value for both
individual …

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