… This paper constructs a three-layer offloading framework in intelligent Internet of Vehicles
(IoV) to … After that, a deep reinforcement learning-based scheme is put forward to solve the …

… vehicle must learn the optimal delivery policy by interacting with the environment. To solve
this problem and optimize the vehicle’s cost, we propose a double deep … , the double deep Q …

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a
plethora of data-intensive and latency-sensitive vehicular applications, posing significant …

… deep deterministic policy gradient (DDPG) algorithm. Especially, we focus on the Internet of
Vehicles … Then, we turn the formulated problem into a reinforcement learning problem and …

… targeting connected vehicle services and applications, also known as Internet of Vehicles (…
, decoupled from the data plane, and uses vehicle location and control traffic load to perform …

… capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), …
intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-…

… For dynamic network clustering, the actor-critic based DRL is employed as the deep
reinforcement learning model in this paper. The reinforcement learning (RL) consists of set of state-…

… the edge cache of the Internet of vehicles with DRL algorithm for … The mobile Internet and
the Internet of Things are deeply … analyzes edge caching for Internet of Vehicles, and proposes …

… vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of
Vehicles … a model-free approach based on deep reinforcement learning (DRL) to solve the …

… Deep Reinforcement Learning Offloading Strategy (DRLOS). The main contributions are as
follows: (i) For the Internet of Vehicles … combines deep learning and reinforcement learning to …