While in [61], the authors recover the true supervision signals with peer loss, which punishes over-agreement for avoiding overfitting.

In this paradigm of centralised training for decentralised execution, QMIX [25] is a popular Qlearning algorithm with state-of-the-art performance ontheStarCraft Multi-Agent Challenge [26]. QMIX represents the optimal joint action value function using a monotonicmixing function of per-agent utilities.

Recurrent experience replay in distributed reinforcement learning.

Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs).

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management.