Pointer-Based Deep Reinforcement Learning Agent for Network Slice Placement in Large Scale Networks

Deep Reinforcement Learning (DRL) has proven effective across numerous fields, making it a promising approach for optimizing network slice placement in 5G mobile networks. Many studies have highlighted the advantages of DRL agents in autonomously and efficiently placing network slices, aligning well with the vision of major operators and organizations for next-generation networks. However, the challenge of stable convergence in DRL agents, particularly when their action space becomes large, has limited their application to small-and medium-sized infrastructures. In this paper, we address this limitation by introducing P-DDQN, a novel pointer-based DRL algorithm that uses a fixed set of navigation actions to enable spatial exploration of the infrastructure. P-DDQN is capable of exploring very large-scale networks while maintaining a constant number of actions. We evaluated it on small, medium, and large infrastructures, and simulation results demonstrate that it outperforms traditional DRL agents, such as Double Deep Q-Network (DDQN) and Deep Q-Network (DQN).