  This paper considers the challenging tasks of Multi-Agent Reinforcement
Learning (MARL) under partial observability, where each agent only sees her own
individual observations and actions that reveal incomplete information about
the underlying state of system. This paper studies these tasks under the
general model of multiplayer general-sum Partially Observable Markov Games
(POMGs), which is significantly larger than the standard model of Imperfect
Information Extensive-Form Games (IIEFGs). We identify a rich subclass of POMGs
-- weakly revealing POMGs -- in which sample-efficient learning is tractable.
In the self-play setting, we prove that a simple algorithm combining optimism
and Maximum Likelihood Estimation (MLE) is sufficient to find approximate Nash
equilibria, correlated equilibria, as well as coarse correlated equilibria of
weakly revealing POMGs, in a polynomial number of samples when the number of
agents is small. In the setting of playing against adversarial opponents, we
show that a variant of our optimistic MLE algorithm is capable of achieving
sublinear regret when being compared against the optimal maximin policies. To
our best knowledge, this work provides the first line of sample-efficient
results for learning POMGs.
