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...

Multi-layered communication networks including satellites and unmanned aerial vehicles (UAVs) with remote sensing capability are expected to be an essential part of next-generation wireless systems. It has been reported that deep reinforcement learning algorithm brings performance improvement in various practical environments. However, it is anticipated the computational complexity will a criti...

We posit a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon any nonlinear function of the team's long-term state-action occupancy measure, i.e., general utility. This subsumes cumulative return but also allows one to incorporate risk-sensitivity, exploration, and priors. derive Decentralized Shadow Reward Actor-Critic (DSAC) which agents alternate between po...

Effective communication can improve coordination in cooperative multi-agent reinforcement learning (MARL). One popular scheme is exchanging agents' local observations or latent embeddings and using them to augment individual policy input. Such a paradigm reduce uncertainty for decision-making induce implicit coordination. However, it enlarges spaces increases complexity, leading poor complex se...

In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas each local in complex plane area. However, due limited communication capabilities between agent, environment becomes partially visi...

In recent years, multi-agent reinforcement learning (MARL) has presented impressive performance in various applications. However, physical limitations, budget restrictions, and many other factors usually impose constraints on a system (MAS), which cannot be handled by traditional MARL frameworks. Specifically, this paper focuses constrained MASes where agents work cooperatively to maximize the ...

molecular diffusion is one of the important contaminant transport mechanisms through soil and rocks. in this study the molecular diffusion coefficient of marl stone and sand stone from the urmia city landfill site was determined using the best fit method of the observed laboratory data and the predicted theoretical data. the marl stone and sand stone samples were tested under the conditions of ...