  This paper introduces four new algorithms that can be used for tackling
multi-agent reinforcement learning (MARL) problems occurring in cooperative
settings. All algorithms are based on the Deep Quality-Value (DQV) family of
algorithms, a set of techniques that have proven to be successful when dealing
with single-agent reinforcement learning problems (SARL). The key idea of DQV
algorithms is to jointly learn an approximation of the state-value function
$V$, alongside an approximation of the state-action value function $Q$. We
follow this principle and generalise these algorithms by introducing two fully
decentralised MARL algorithms (IQV and IQV-Max) and two algorithms that are
based on the centralised training with decentralised execution training
paradigm (QVMix and QVMix-Max). We compare our algorithms with state-of-the-art
MARL techniques on the popular StarCraft Multi-Agent Challenge (SMAC)
environment. We show competitive results when QVMix and QVMix-Max are compared
to well-known MARL techniques such as QMIX and MAVEN and show that QVMix can
even outperform them on some of the tested environments, being the algorithm
which performs best overall. We hypothesise that this is due to the fact that
QVMix suffers less from the overestimation bias of the $Q$ function.
