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Furthermore, they concentrated on the model-free reinforcement learning approach to provide training guidelines based on a large number of historical experiences. This model-free approach is divided into three categories: critic-model (value-based approach), actor-model (policy-based approach), and actor-critic learning approach, which employs deep neural networks to provide an accurate estimation of deterministic policy function and value function.
Chen et al.  proposed a task offloading scheme based solely on V2V communication, based on the gathered period of vehicles in urban environments due to traffic lights or areas of interest (AOI) to minimize task processing time. The Max-Min Fairness scheme is used to optimize the task execution time, which is then solved by the particle swarm optimization (PSO) algorithm. On the one hand, for the special case where all service vehicles participate in task processing, the proposed algorithm provides the optimal solution based on adapted Max-Min Fairness. On the other hand, the PSO algorithm is used for generating a feasible solution for the general case where it is unknown whether each service vehicle will participate in task processing or not. Furthermore, to evaluate the performance of their proposed scheme, they generated vehicle track files using the TIGER map and then used the IDM IM model provided by VanetMobiSim to compare the performance of computed schemes such as local computing, offloading with the Max-Min Fairness Algorithm, and offloading with the PSO Algorithm. The results showed that the Max-Min Fairness algorithm and the PSO algorithm reduced task execution time based on the number of service vehicles used. Additionally, the increasing number of service vehicles showed that the PSO algorithm is slightly better than the Max-Min Fairness algorithm in terms of robustness over different task sizes for each scheme.