# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 3 Admission Control
### Admission Based on RL and DRL  
The following paragraphs review recent RL and DRL investigations in NSL or related technologies that perform admission control. Ref. [38] introduced an admission control algorithm called N3 AC designed to maximize the price per time unit paid by inelastic and elastic network slices. Inelastic network slices were associated with uRLLC services. Elastic network slices were related to eMBB and mMTC services. N3 AC trained two DNNs without a ground truth sequence (the proper sequence is unknown a priori); a DNN was used to estimate the revenue for each state when the action is to accept. The other DNN is for the rejecting action. In addition, N3 AC modeled the state space as a three-sized tuple (Ne, Ni, k), where Ne and Ni are the numbers of inelastic and elastic slices, and k is the next event that indicates the arrival request or departure of a network slice. The space of actions was represented binary to admitting or rejecting new inelastic and elastic requests. The performance evaluation was performed in Keras/TensorFlow and included two elastic and two inelastic slices arriving by following a Poisson process. The time life of slices followed an exponential distribution. Ref. [39] presented a SARSA (state-action-reward-state-action)-based cross-slice admission framework devised to maximize the operators' revenue taking into account constraints related to communication, computing, and storage resources. The framework modeled the space of states considering demanded and deployed slices (best-effort and supporting QoS) and the resources available in the RAN and CN of 5G. The SARSA agent operated with a space of actions conceived as the number of slices to accept; it selected actions using the e-greedy method. The performance evaluation involved simulations in a nonspecified tool and six templates of slices not following the 5G use cases' specific requirements. Refs. [40, 41] proposed a RL-based admission control approach that uses DQN to maximize the providers' total profit when dealing with low-profit services (e.g., on-demand media streaming and file transfer) and high-profit services (e.g., immersive media). The approach modeled the space of states regarding the available resources in the 5G-RAN. The DQN agent employed a space of actions involving holding time, service priority, and resources required. The performance evaluation included a custom-built Python-based event-driven simulator that used a networkX library for the graph representation and management of network resources and the Keras tool to implement the stochastic policy network. Ref. [43] introduced an admission control mechanism based on R-learning [137] and Q-learning to maximize the long-term average profit in multidomain 5G-CNs. The mechanism modeled the space of states regarding the demand in the consumer and provider domains. The space of actions was defined as accepting service requests when the available capacity in the provider is greater than the total amount of demanded resources and rejecting in other cases. Ref. [44] proposed a DQN-based admission control approach for improving 5G radio resource management and enhancing isolation in three types of slices: best effort, constant bit-rate, and minimum bit-rate. The approach included two DNNs (online and target) in the DQN model to generate a learning policy that maximizes the cumulative reward. Online and target DNNs used an ReLU [138] with three layers of 50, 50, and 100 neurons and were employed to reduce errors in estimations. The DQN model defined the space of states in terms of key performance indicators (e.g., throughput, dropping rate, and admission rate). The space of actions was modeled considering control parameters for increasing or  
Figure 3: RL/DRL-based admission control architecture. decreasing resources to the slices. Ref. [42] presented an admission control mechanism based on DQN with online and target DNNs for maximizing the providers' revenue and minimizing the penalty cost caused by SLA violations in 5G-RAN.