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

We propose universal randomized function approximation-based empirical valueiteration (EVI) algorithms for Markov decision processes. The `empirical'nature comes from each iteration being done empirically from samples availablefrom simulations of the next state. This makes the Bellman operator a randomoperator. A parametric and a non-parametric method for function approximationusing a parametric function space and the Reproducing Kernel Hilbert Space(RKHS) respectively are then combined with EVI. Both function spaces have theuniversal function approximation property. Basis functions are picked randomly.Convergence analysis is done using a random operator framework with techniquesfrom the theory of stochastic dominance. Finite time sample complexity boundsare derived for both universal approximate dynamic programming algorithms.Numerical experiments support the versatility and effectiveness of thisapproach.

An Empirical Dynamic Programming Algorithm for Continuous MDPs