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

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning