4 天前 - … of reinforcement learning, and the limitations of current approaches. We present
the commonalities of mobile apps and web apps as the intuition to complete this paper. …

7 天前 - … mobile edge computing, this paper proposes a task offloading and path optimization
approach via muti-agent deep reinforcement learning. … -assisted mobile edge computing …

7 天前 - … networks. This can pose a design limitation for the agents such as autonomous (mobile) …
MADDPG [12] is a classic reinforcement learning method in MAS that does not use …

7 天前 - … Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and
mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (…

7 天前 - … unit cell, providing precise localization of mobile devices across the network. This
input, … using the reinforcement learning method [26, 27, 28]. Reinforcement Learning is very …

8 天前 - … to large transmission delays, while general mobile users and devices such as Virtual
… in mobile networks and backbone networks [3]. To this end, technologies such as Mobile …

8 天前 - … -user multi-access mobile edge computing (MEC) network where users split their
tasks … the theoretical derivation and the deep reinforcement learning (DRL) algorithm to solve …

8 天前 - … to efficiently utilize all network computing resources to … container migration strategy
for mobile users to minimize … 2) We introduce a reinforcement learning algorithm based …

8 天前 - … mobile robot of any contact in a 360 zone around the robot as well as the magnitude
of the contact force. • an open-source reinforcement learning (… omnidirectional mobile robot …

9 天前 - … reinforcement learning (CRL) algorithm that facilitates knowledge collaboration
among agents based on the policy distribution of the actor-network. • … in 6g mobile networks via …