## 1 Introduction
### Related Works
The joint optimization of transmission duration and workload offloading allocation between the edge servers achieves minimized computational delay in downlink NOMA [23]. In the past decade, technologies have evolved persistently to attain smartness in vehicles. It has been realized that processing huge amounts of real-time data with ultra-reliability and delay-sensitivity requirements while possessing limited computational ability in vehicles becomes cumbersome. Thereby, offloading the computations and tasks to the edge cloud and vehicular cloudlet appears to be viable. Simultaneously, Artificial Intelligence (AI) has profoundly revolutionized the living, specifically in ITS. The prominent achievements of AI in vehicular networks owe to the rise of Reinforcement Learning (RL) and its subfield, Deep Reinforcement Learning (DRL). An extensive review of studies that combine RL/DRL strategies to coordinate vehicular networks, particularly focusing on challenges in vehicular telecommunication, is presented in [24]. Besides, considering the bounded potential of edge nodes, cloud-edge services the offloading decisions. Consequently, a multi-update RL algorithm is formulated for decision-making in delay-sensitive applications in [25]. Despite MEC delivering computational capabilities in the near vicinity, modeling the offloading policies is crucial. An optimization problem is formulated for offloading decision-making based on channel characteristics between UE and BS in [26]. Ultimately, Deep Reinforcement Learning algorithms are applied for optimizing the decisions on task-partitioning and offloading resource management scenarios in MEC. The obtained decision variables are handled individually, impacting the system performance. To surmount the issue and jointly optimize the acquired decision variables, the hybrid action space is addressed. A D3 PG model is developed to deal with multi-objective optimization along with task processing maximization, including energy and delay minimization, in [27]. Although DRL techniques are employed for resource management in V2X communication, certain vulnerabilities remain unsolved, including quantization errors caused by hybrid action space and high computational complexity by multi-dimensional quantization [28]. The literature considered thus far has examined the computation offloading and resource management in MEC for quasi-static systems; following this, dynamic MEC systems are attempted. A joint optimization algorithm for offloading computations and allocating resources in a rapidly changing MEC demanding heterogeneous services with delay constraints is devised [29]. Furthermore, intelligent resource management schemes are a significant prerequisite in vehicle platooning for communicating CAM messages between vehicles frequently at regular intervals. A joint optimization algorithm for the resource management and control factors of vehicles in the LTE-V2V network is proposed in [30]. The cell border limitations with the LTE mobility system are addressed by employing 3 GPP V2X deployment. These feature enhancements of 3 GPP NRV2X are incorporated in Rel 16 and Rel 17 [31]. As the deployment of MEC is regarded in a cellular environment, the high mobility of users creates a bottleneck hindering efficiency and reliability. An optimization technique to overcome the migration/handover challenges under distinct circumstances is proposed in [32]. Usually, the data size for each user-requested computational task differs. It is the responsibility of the BS to efficiently regulate the resource allocation strategy; so, an optimization problem based on a multi-stack reinforcement learning algorithm is formulated to minimize the computation and communication delays between each user [33]. The computation efficiency, termed as the trade-off between computational time and consumed energy, is analyzed in a Vehicular Edge Computing scenario, and the MACTER algorithm is proposed to solve the distinct requirements of the mentioned scenario in [34].