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 prove a fundamental limitation on the efficiency of a wide class ofReinforcement Learning (RL) algorithms. This limitation applies to model-freeRL methods as well as a broad range of model-based methods, such as planningwith tree search.  Under an abstract definition of this class, we provide a family of RLproblems for which these methods suffer a lower bound exponential in thehorizon for their interactions with the environment to find an optimalbehavior. However, there exists a method, not tailored to this specific familyof problems, which can efficiently solve the problems in the family.  In contrast, our limitation does not apply to several types of methodsproposed in the literature, for instance, goal-conditioned methods or otheralgorithms that construct an inverse dynamics model.

Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned