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Found 4 papers, 0 papers with code
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL).
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering
These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs.
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the probability density function of the outliers.
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
This study addresses the problem of selecting dynamically, at each time instance, the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the potentially time-varying probability distribution function of the outliers.
