… Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for
… DRL-based method, which we call DRL-based energyefficient control for coverage and …

… Hence, a learning approach is required to … learning approaches is reinforcement learning
(RL); hence, we start by reviewing RL and then introduce the proposed learning approach. …

… communication coverage for … deep reinforcement learning (DRL) based framework to control
each UAV in a distributed manner. Our goal is to maximize the temporal average coverage …

… that the underlying communication network is always connected to enable information
exchange and cooperative coverage control. Deep reinforcement learning based framework is …

… To this end, we propose to leverage AI technique particularly Deep Reinforcement Learning
(… effective and fair communication coverage: A deep reinforcement learning approach,” IEEE …

… , reinforcement learning, and deep learning techniques which are important branches of
machine learning theory… the deep learning to improve efficiency and performance in terms of the …

… deployed to provide communication coverage to the ground users in the target area. Because
the number of UAVs is limited, the users cannot be completely covered by hovering UAVs. …

… the coverage of multiple UAVs in urban areas. In [11], the authors presented an … approach
where a minimal number of UAVs are deployed to improve the communication coverage for …

… coverage trajectory algorithm to carry out specific coverage tasks within patches based on the
star communication … a swarm deep Q-learning (SDQN) reinforcement learning algorithm to …

… deep reinforcement learning for proactive caching[34-36] and coded caching[41]. We observe
that deep reinforcement learning … communication coverage: A deep reinforcement learning …