User Plane Selection for Core Networks using Deep Reinforcement Learning

Sammanfattning: Allocating service functions to a core network upon users’ various demands isof importance in 5G networks. In this thesis work, we have studied reinforcementlearning models to solve this allocation problem. More precisely, 1) webuild a simple version of an MDP model for allocation in 5G core networks,2) we train an agent using a family of deep-Q learning (DQN) algorithms.When the number of nodes in the core network is large, one critical challengeis overcoming the sampling inefficiency due to a high dimensional actionspace, i.e., most of the exploratory allocations made by the agent gives zeroreward. To deal with such reward sparsity, we applied two techniques: prioritizedexperience replay (PER) and hindsight experience replay (HER).Our study shows that a DQN agent trained with both HER and PER providesa reasonable allocation in a larger sized networks, whereas a vanillaDQN agent works only for a very limited case where the number of nodesis small.