  In many real-world settings, a team of agents must coordinate their behaviour
while acting in a decentralised way. At the same time, it is often possible to
train the agents in a centralised fashion in a simulated or laboratory setting,
where global state information is available and communication constraints are
lifted. Learning joint action-values conditioned on extra state information is
an attractive way to exploit centralised learning, but the best strategy for
then extracting decentralised policies is unclear. Our solution is QMIX, a
novel value-based method that can train decentralised policies in a centralised
end-to-end fashion. QMIX employs a network that estimates joint action-values
as a complex non-linear combination of per-agent values that condition only on
local observations. We structurally enforce that the joint-action value is
monotonic in the per-agent values, which allows tractable maximisation of the
joint action-value in off-policy learning, and guarantees consistency between
the centralised and decentralised policies. We evaluate QMIX on a challenging
set of StarCraft II micromanagement tasks, and show that QMIX significantly
outperforms existing value-based multi-agent reinforcement learning methods.
