Scheduling the operation of a connected vehicular network using deep reinforcement learning
Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
Deep reinforcement learning-based scheduling for roadside communication networks
The proper design of a vehicular network is the key expeditor for establishing an efficient
Intelligent Transportation System, which enables diverse applications associated with traffic …
Intelligent Transportation System, which enables diverse applications associated with traffic …
Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-based Deep Reinforcement Learning Approach
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach
The developments of connected vehicles are heavily influenced by information and
communications technologies, which have fueled a plethora of innovations in various areas …
communications technologies, which have fueled a plethora of innovations in various areas …
Resource allocation for delay-sensitive vehicle-to-multi-edges (V2Es) communications in vehicular networks: A multi-agent deep reinforcement learning approach
The rapid development of internet of vehicles (IoV) has recently led to the emergence of
diverse intelligent vehicular applications such as automatic driving, auto navigation, and …
diverse intelligent vehicular applications such as automatic driving, auto navigation, and …
Collaborative data scheduling for vehicular edge computing via deep reinforcement learning
With the development of autonomous driving, the surging demand for data communications
as well as computation offloading from connected and automated vehicles can be expected …
as well as computation offloading from connected and automated vehicles can be expected …
A generative adversarial network enabled deep distributional reinforcement learning for transmission scheduling in internet of vehicles
The Cognitive Internet of Vehicles (CIoV) is an intelligent network that embeds the cognitive
mechanism in the Internet of Vehicles (IoV) to sense the environment and observe the …
mechanism in the Internet of Vehicles (IoV) to sense the environment and observe the …
Deep reinforcement learning (DRL)-based resource management in software-defined and virtualized vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) have attracted great interests from both industry and
academia. The developments of VANETs are heavily influenced by information and …
academia. The developments of VANETs are heavily influenced by information and …
Cluster-enabled cooperative scheduling based on reinforcement learning for high-mobility vehicular networks
It is important to transmit data reliably, and efficiently in vehicular networks. Existing works
usually study routing strategies, and cooperative scheduling to improve the efficiency of …
usually study routing strategies, and cooperative scheduling to improve the efficiency of …
Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing
Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …