Spectrum allocation in the form of primary channel and bandwidth selection is a key factor
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

In this paper, we address the problem of distributed interference management of femtocells
that share the same frequency band with macrocells using distributed multi‐agent Q …

Sixth generation (6G) in-X subnetworks are recently proposed as short-range low-power
radio cells for supporting localized extreme wireless connectivity inside entities such as …

Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of
exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and …

Reinforcement learning (RL) has demonstrated remarkable achievements in simulated
environments. However, carrying this success to real environments requires the important …

This paper studies a class of multi-agent reinforcement learning (MARL) problems where the
reward that an agent receives depends on the states of other agents, but the next state only …

There has recently been a surge in research in batch Deep Reinforcement Learning (DRL),
which aims for learning a high-performing policy from a given dataset without additional …

We consider the effect of the mean availability distribution of primary channels on the
performance of distributed learning and access policies, and develop a distributed learning …