… Towards this end, we propose a deep reinforcement learning-based online offloading (…
computational complexity does not explode with the network size. 2) Unlike many existing deep …

… learning in mobile edge computing networks that operate with finite … deep reinforcement
learning method, namely deep Q-learning, we design an intelligent agent at the edge computing …

… In this paper, we propose a VEC network to enhance the … ) We propose a vehicle edge
computing network architecture in … for the vehicle edge computing network while considering both …

… characteristics make UAV networks more vulnerable to … UAV networks, we present a deep
reinforcement learning approach to detect malicious attacks in UAV aerial computing networks…

… computing (MEC) networks under dynamic edge environment. In this paper, we consider a
multi-user MEC network … algorithm to maximize the network data processing capability subject …

… migration and computing cooperation schemes, we present a computing collaboration …
MEC-enabled vehicular network. Once an edge server receives the computing tasks offloaded by …

… We also develop a deep reinforcement learning process to deal with large network state
space, real-time network state transitions, and massive amounts of arriving applications. …

… mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement
learning algorithm for making resource allocation decisions based on network states and …

… networks to achieve automatic resource allocation [18], [19]. In this paper, deep reinforcement
… policy in vehicular networks with integrated networking, caching, and computing. 4) …

… At this point, how to allocate computing resources and network … Deep Reinforcement
Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network …