In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

… 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 …

… communication technology, this study applies DNNs and DRL algorithms to wireless networks,
providing experimental basis for the development of the wireless communication industry. …

… We recall that DQL is a popular method of reinforcement learning (RL) that incorporates
a deep neural network (DNN) as an approximator of Q(.) function to seek the optimal actions …

… To find an optimal solution in a highly random environment we propose reinforcement learning
methods, such as deep deterministic policy gradient (DDPG) and wolpertinger DDPG (W-…

… We proposed a unified reinforcement learning solution for finding the jointly optimal
power-control, AMC, and DPM policies when the traffic arrival and channel statistics are unknown. …

… Reinforcement Learning (DRL) on communication flow control in WMNs. Moreover, different
from a general DRL based networking solution, in which the network … Q-learning Network (…

… protocol for Wireless Sensor Networks (WSN) that employs a reinforcement learning framework…
In this paper, nodes actively infer the state of other nodes, using a reinforcement learning …

… networks. To circumvent the combinatorial nature of searching in the huge strategy space,
we invoke a reinforcement learning … We propose two reinforcement learning strategy update …

… We consider a wireless network where multiple stations use the same MAC protocol to
compete for channel access. The smallest time unit in the simulation is time slot, which is 9μs …