Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as team robots with cameras, could work collaboratively to perceive the environment for efficient and accurate situation awareness. However, these agents often limited computation, communication, storage resources. Thus, reducing resource consumption while still providi...

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of machine learning (QML) looming era practical computing. Motivated by this, this article we re-design multi-agent reinforcement (MARL) based on unique characteristics neural networks (QNNs) having two separate dimensions trainable parameters: angle parameters affecting output...

The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves best performance on StarCraft II micromanagement testbed, common MARL benchmark. However, our experiments demonstrate that, some cases, performs sub-optimally with A2C framework, ...