  QMIX is a popular $Q$-learning algorithm for cooperative MARL in the
centralised training and decentralised execution paradigm. In order to enable
easy decentralisation, QMIX restricts the joint action $Q$-values it can
represent to be a monotonic mixing of each agent's utilities. However, this
restriction prevents it from representing value functions in which an agent's
ordering over its actions can depend on other agents' actions. To analyse this
representational limitation, we first formalise the objective QMIX optimises,
which allows us to view QMIX as an operator that first computes the
$Q$-learning targets and then projects them into the space representable by
QMIX. This projection returns a representable $Q$-value that minimises the
unweighted squared error across all joint actions. We show in particular that
this projection can fail to recover the optimal policy even with access to
$Q^*$, which primarily stems from the equal weighting placed on each joint
action. We rectify this by introducing a weighting into the projection, in
order to place more importance on the better joint actions. We propose two
weighting schemes and prove that they recover the correct maximal action for
any joint action $Q$-values, and therefore for $Q^*$ as well. Based on our
analysis and results in the tabular setting, we introduce two scalable versions
of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW)
QMIX and demonstrate improved performance on both predator-prey and challenging
multi-agent StarCraft benchmark tasks.

Machine Learning;Multiagent Systems;Machine Learning