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The purpose of this paper is to develop a self-optimized associationalgorithm based on PGRL (Policy Gradient Reinforcement Learning), which is bothscalable, stable and robust. The term robust means that performance degradationin the learning phase should be forbidden or limited to predefined thresholds.The algorithm is model-free (as opposed to Value Iteration) and robust (asopposed to Q-Learning). The association problem is modeled as a Markov DecisionProcess (MDP). The policy space is parameterized. The parameterized family ofpolicies is then used as expert knowledge for the PGRL. The PGRL convergestowards a local optimum and the average cost decreases monotonically during thelearning process. The properties of the solution make it a good candidate forpractical implementation. Furthermore, the robustness property allows to usethe PGRL algorithm in an "always-on" learning mode.

The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach