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

Deliberating on large or continuous state spaces have been long standingchallenges in reinforcement learning. Temporal Abstraction have somewhat madethis possible, but efficiently planing using temporal abstraction still remainsan issue. Moreover using spatial abstractions to learn policies for varioussituations at once while using temporal abstraction models is an open problem.We propose here an efficient algorithm which is convergent under linearfunction approximation while planning using temporally abstract actions. Weshow how this algorithm can be used along with randomly generated option modelsover multiple time scales to plan agents which need to act real time. Usingthese randomly generated option models over multiple time scales are shown toreduce number of decision epochs required to solve the given task, henceeffectively reducing the time needed for deliberation.

Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options