  In cooperative multi-agent systems, agents jointly take actions and receive a
team reward instead of individual rewards. In the absence of individual reward
signals, credit assignment mechanisms are usually introduced to discriminate
the contributions of different agents so as to achieve effective cooperation.
Recently, the value decomposition paradigm has been widely adopted to realize
credit assignment, and QMIX has become the state-of-the-art solution. In this
paper, we revisit QMIX from two aspects. First, we propose a new perspective on
credit assignment measurement and empirically show that QMIX suffers limited
discriminability on the assignment of credits to agents. Second, we propose a
gradient entropy regularization with QMIX to realize a discriminative credit
assignment, thereby improving the overall performance. The experiments
demonstrate that our approach can comparatively improve learning efficiency and
achieve better performance.
