Nonparametric Bellman Mappings for Reinforcement Learning: A Robust Adaptive Filtering Solution

This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL) and applies them to offer a solution for countering outliers in adaptive filtering, without any prior knowledge on the statistics of the outliers and without training data.