… 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. …

… 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 …

… 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…

… 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 …

… 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 …

… deep reinforcement learning for proactive caching[34-36] and coded caching[41]. We observe
that deep reinforcement learning … The sequence-to-sequence learning model can also be …

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

… of reinforcement learning methods in different wireless IoT … for solving the network and
application problems in wireless IoT, … an overview of some wireless IoT networks. Section 3 …

… We consider a wireless network consisting of a single AP that services N users in the
presence of a jammer, as shown in Fig. 1. The users and jammer are uniformly distributed in the …

… Reinforcement Learning (RL) is a sub-area of machine learning … reinforcement learning
based mac protocol for wireless sensor networks,” International Journal of Sensor Networks…