  This paper introduces Greedy UnMix (GUM) for cooperative multi-agent
reinforcement learning (MARL). Greedy UnMix aims to avoid scenarios where MARL
methods fail due to overestimation of values as part of the large joint
state-action space. It aims to address this through a conservative Q-learning
approach through restricting the state-marginal in the dataset to avoid
unobserved joint state action spaces, whilst concurrently attempting to unmix
or simplify the problem space under the centralized training with decentralized
execution paradigm. We demonstrate the adherence to Q-function lower bounds in
the Q-learning for MARL scenarios, and demonstrate superior performance to
existing Q-learning MARL approaches as well as more general MARL algorithms
over a set of benchmark MARL tasks, despite its relative simplicity compared
with state-of-the-art approaches.
