… In this section, the deep reinforcement learning based resource … V2V communications is
introduced. The formulations of key parts in the reinforcement learning are shown and the deep …

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V)
communication in the absence of a base station. Each vehicle autonomously selects transmission …

… In this paper, we investigate a novel federated multi-agent deep reinforcement learning (…
for V2V communication. The approach takes advantage of both deep reinforcement learning (…

… Unlike in our previous work, where we used beaconing rate and transmission power [19]
and the MDP was solved using tabulated policies, in this work we apply Deep Reinforcement …

… This paper proposes a new radio resources allocation system for V2V communications based
… deep reinforcement learning to allocate the radio resources in vehicular communications. …

… deep reinforcement learning (DRL)-assisted intelligent beam management method for
vehicle-to-vehicle (V2V) communication. … complex and fluctuating communication scenarios at the …

… communications and the reliability of V2V communications were … In [12], a deep reinforcement
learning (DRL)-based … was developed for V2V communications, and each V2V transmitter …

… In this section, the framework of deep reinforcement learning (DRL) for mode selection and
power adaptation in V2V communications is introduced, including the representation of the …

… ‘‘distributed deep deterministic policy gradient’’ and ‘‘sharing deep … -based V2V
communications. Numerical results show that our proposed models outperform other deep …

… We deal with the objective of providing reliable V2V communications in DOCA by using an
RL-based scheduling approach. Our solution involves an RL agent, a logically centralized …