Multi-Agent Reinforcement Learning with Pointer Networks for Network Slicing in Cellular Systems

In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations. We first introduce the wireless network virtualization (WNV) and the interference channel model. Then, we formulate the network slicing problem in the dynamic environment in which fading varies, users have mobility, and requests are randomly generated over time. Subsequently, we propose a deep RL framework with multiple actors and centralized critic (MACC) to maximize the reward over all base stations instead of pursuing local optimization. The actors are implemented as pointer networks to fit the varying dimension of input. Finally, we evaluate the performance of the proposed deep RL algorithm via simulations to demonstrate its effectiveness.