English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper aims at the algorithmic/theoretical core of reinforcement learning(RL) by introducing the novel class of proximal Bellman mappings. Thesemappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefitfrom the rich approximation properties and inner product of RKHSs, they areshown to belong to the powerful Hilbertian family of (firmly) nonexpansivemappings, regardless of the values of their discount factors, and possess ampledegrees of design freedom to even reproduce attributes of the classical Bellmanmappings and to pave the way for novel RL designs. An approximatepolicy-iteration scheme is built on the proposed class of mappings to solve theproblem of selecting online, at every time instance, the "optimal" exponent $p$in a $p$-norm loss to combat outliers in linear adaptive filtering, withouttraining data and any knowledge on the statistical properties of the outliers.Numerical tests on synthetic data showcase the superior performance of theproposed framework over several non-RL and kernel-based RL schemes.

Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering