… 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 …

… framework for reinforcement learning tasks with constraints, where agents learn an action …
For our proposed movement algorithm, we introduce neural networks into Q-learning, which …

… RL formulation We introduce the reinforcement learning formulation of the wireless network
optimization problem in this section. 1) Reward function: It is well known that designing a …

… PHY-layer authentication in dynamic wireless networks, which compares the channel states
… threshold based on reinforcement learning, in dynamic wireless networks without knowing …

We consider a mobile user accessing contents in a dynamic environment, where new
contents are generated over time (by the user's contacts) and remain relevant to the user for …

… reinforcement learning algorithm that uses deep Q network to … to implement deep reinforcement
learning in this paper to … -enabled opportunistic IA wireless networks. Simulation results …

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

… 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 …

… called Reinforcement learning (RL) [2] to various schemes in WSNs in order to improve
network performance. The RL approach adopts an unsupervised and online learning technique. …

… Abstract—This paper investigates the use of deep reinforcement learning (DRL) to solve
the dynamic spectrum access problem. Specifically, we examine the scenario where multiple …