  Multi-agent reinforcement learning (MARL) has recently received considerable
attention due to its applicability to a wide range of real-world applications.
However, achieving efficient communication among agents has always been an
overarching problem in MARL. In this work, we propose Variance Based Control
(VBC), a simple yet efficient technique to improve communication efficiency in
MARL. By limiting the variance of the exchanged messages between agents during
the training phase, the noisy component in the messages can be eliminated
effectively, while the useful part can be preserved and utilized by the agents
for better performance. Our evaluation using a challenging set of StarCraft II
benchmarks indicates that our method achieves $2-10\times$ lower in
communication overhead than state-of-the-art MARL algorithms, while allowing
agents to better collaborate by developing sophisticated strategies.
