ucture was proposed to allocation reach an efficient capacity forecast rather than the prediction and legacy mobile traffic prediction optimization [191] Maximization Deploy RAN resources allocation independent of reinforcement of the slice the fluctuant number of RAN slices A DRL method learning number was proposed to optimize the RAN resource allocation [192] E2E QoS Dynamic resource allocation to maximize the Q-learning optimization number of access users. An E2E network slicing resource allocation algorithm based on deep Q-learning has been proposed to optimize the resource allocation on RAN and core sides [193] Optimization The proposed RL algorithm optimizes the slice reinforcement request partitioning for a slice deployed across learning multi-provider multiple substrate networks with outperformance in physical terms of delay infrastructures [194] Reinforcement Slice RL-based solution to maximize the profit of the learning admission provider infrastructure with different QoS optimization constraints. on RAN A RL-based slice admission policy was proposed to infrastructure maximize the provider infrastructure revenues by minimizing the rejected or degraded services [195] LSTM & A3C RAN slicing Provide performance isolation among RAN slices algorithm optimization and optimize the RAN resources. A collaborative learning framework using DL for large timescale networks in conjunction with RL for small timescale networks has been proposed and solved by asynchronous advantage actor critic (A3C) algorithm [196] Deep neural RAN slicing A deep Q-network was proposed for the dynamic network optimization radio resource slicing allocation. This algorithm improves significantly the radio resource utilization [197] Deep neural Dynamic SFC Optimization of SFC dynamic resource allocation network resource for multilayer core network. allocation RL algorithm was proposed to enhance the performance of dynamic SFC resources allocation A Comprehensive Study on 5G profits of the infrastructure provider and to satisfy the tenant-u