Constrained Reinforcement Learning for Resource Allocation in Network Slicing

In network slicing, dynamic resource allocation is the key to network performance optimization. Deep reinforcement learning (DRL) is a promising method to exploit the dynamic features of network slicing by interacting with the environment. However, the existing DRL-based resource allocation solutions can only handle a discrete action space. In this letter, we tackle a general DRL-based resource al...