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

Many reinforcement learning (RL) environments in practice feature enormousstate spaces that may be described compactly by a "factored" structure, thatmay be modeled by Factored Markov Decision Processes (FMDPs). We present thefirst polynomial-time algorithm for RL in Factored State MDPs (generalizingFMDPs) that neither relies on an oracle planner nor requires a lineartransition model; it only requires a linear value function with a suitablelocal basis with respect to the factorization, permitting efficient variableelimination. With this assumption, we can solve this family of Factored StateMDPs in polynomial time by constructing an efficient separation oracle forconvex optimization. Importantly, and in contrast to prior work on FMDPs, we donot assume that the transitions on various factors are conditionallyindependent.

Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions