Constrained Reinforcement Learning for Resource Allocation in Network Slicing

Abstract

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 allocation problem which considers a mixed action space including both discrete channel allocation and continuous energy harvesting time division, with the constraints of energy consumption and queue package length. We propose a novel DRL algorithm referred to as constrained discrete-continuous soft actor-critic (CDC-SAC) by redesigning the network architecture and policy learning process. Simulation results show that the proposed algorithm can achieve a significant performance improvement in terms of the total throughput with the strict constraints guarantee.