… as enabling techniques for AI-based wireless networks and we focus on delivering a more
applied perspective of MARL to solve wireless communication problems. Table II summarizes …

… , we apply the concept of reinforcement learning and implement a deep Q-network (DQN).
We … Finally, we propose an adaptive DQN approach with the capability to adapt its learning in …

… tions in wireless networks in terms of network layers. Based on this, we illustrate how the
researchers tailor RL to address various control problems across different network layers. …

… able to the traditional reinforcement learning (RL) [5] for wireless networking. Specifically, …
deep neural networks (DNN) in DRL affords us with two essential properties to wireless MAC: (i…

… reinforcement learning to power control [8]. Sun et al. [9] proposed a centralized supervised
learning approach to train a fast deep neural network (… compare the reinforcement learning …

… Reinforcement Learning (RL) has been shown to address this routing challenge by enabling
wireless … four types of distributed wireless networks, namely wireless ad hoc networks, …

… for intelligent wireless communications have obtained widespread attention, among which
deep reinforcement learning (DRL) is an excellent machine learning technology. DRL has …

… network-wide performance enhancement they have to offer. In this article, we advocate the
use of reinforcement learning (… dynamic channel selection in wireless networks. Examples of …

… management and interference mitigation in wireless networks using multi-agent deep
reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that …

… For example, we know that the expected transmission time of a packet in a wireless
network is 20 minutes. However, this information may not be so meaningful because it may …