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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In batch reinforcement learning, there can be poorly explored state-actionpairs resulting in poorly learned, inaccurate models and poorly performingassociated policies. Various regularization methods can mitigate the problem oflearning overly-complex models in Markov decision processes (MDPs), howeverthey operate in technically and intuitively distinct ways and lack a commonform in which to compare them. This paper unifies three regularization methodsin a common framework -- a weighted average transition matrix. Consideringregularization methods in this common form illuminates how the MDP structureand the state-action pair distribution of the batch data set influence therelative performance of regularization methods. We confirm intuitions generatedfrom the common framework by empirical evaluation across a range of MDPs anddata collection policies.

Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning