  Centralised training with decentralised execution is an important setting for
cooperative deep multi-agent reinforcement learning due to communication
constraints during execution and computational tractability in training. In
this paper, we analyse value-based methods that are known to have superior
performance in complex environments [43]. We specifically focus on QMIX [40],
the current state-of-the-art in this domain. We show that the representational
constraints on the joint action-values introduced by QMIX and similar methods
lead to provably poor exploration and suboptimality. Furthermore, we propose a
novel approach called MAVEN that hybridises value and policy-based methods by
introducing a latent space for hierarchical control. The value-based agents
condition their behaviour on the shared latent variable controlled by a
hierarchical policy. This allows MAVEN to achieve committed, temporally
extended exploration, which is key to solving complex multi-agent tasks. Our
experimental results show that MAVEN achieves significant performance
improvements on the challenging SMAC domain [43].
