… reinforcement learning is used to obtain the resource allocation policy in vehicular networks
with integrated networking… present the network model, followed by the communication model…

… , they often discover elegant communication protocols along the way. To … communication
or reinforcement learning with deep neural networks has succeeded in learning communication …

… Reinforcement Learning (DRL) [7] as an enhanced version of RL. Based on DRL, we propose
a Deep Q Network (DQN) which can use a Deep Neural Network (… reinforcement learning …

… , Networked MG generalizes the MG framework to model cooperative agents with different
reward functions by leveraging shared information through a communication network (see …

… reinforcement learning (DRL)-based MAC protocol for heterogeneous wireless networking,
referred to as a Deep-reinforcement Learning … a number of networks operating different MAC …

… -control algorithm using reinforcement learning in a VSN. In our algorithm, the BBU pool
uses centralized Q-learning, and the vehicles use distributed Q-learning to achieve improved …

… In this paper, we present a reinforcement learning framework for … We present a comprehensive
design of the learning … System level simulations show that reinforcement learning based …

… and LSTM is utilized to learn network contention states. … communication networks
using multiagent RL. It is formulated as a stochastic game and then solved by a multiagent Q-learning, …

… a network often become intractable. Accordingly, in this paper, we present a distributed DSA
based communication framework based on multi-agent reinforcement learning (RL), where …

Energy harvesting (EH) two-hop communications are … knowledge, the two-hop communication
problem can be separated into … To find the power allocation policy, reinforcement learning …