… , 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. …

… 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 …

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

… deep reinforcement learning approach to wireless networks … to implement deep reinforcement
learning in this chapter to … interference alignment wireless networks. Simulation results are …

… 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 …

… and coordination, to wireless networks. We discuss how several wireless network schemes
have been approached using RL to provide network performance enhancement, and also …

… 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 …

… at the wireless network edge using a deep reinforcement learning framework with
Wolpertinger architecture. In particular, we propose deep actorcritic reinforcement learning based …