The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach
This addresses the association problem in wireless networks for improved network performance, but it appears incremental as it applies an existing PGRL method to this specific domain.
The paper tackles the association problem in wireless networks by developing a self-optimized algorithm using Policy Gradient Reinforcement Learning (PGRL), which is scalable, stable, and robust, with the algorithm converging to a local optimum and showing monotonic decrease in average cost during learning.
The purpose of this paper is to develop a self-optimized association algorithm based on PGRL (Policy Gradient Reinforcement Learning), which is both scalable, stable and robust. The term robust means that performance degradation in the learning phase should be forbidden or limited to predefined thresholds. The algorithm is model-free (as opposed to Value Iteration) and robust (as opposed to Q-Learning). The association problem is modeled as a Markov Decision Process (MDP). The policy space is parameterized. The parameterized family of policies is then used as expert knowledge for the PGRL. The PGRL converges towards a local optimum and the average cost decreases monotonically during the learning process. The properties of the solution make it a good candidate for practical implementation. Furthermore, the robustness property allows to use the PGRL algorithm in an "always-on" learning mode.