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

This paper explores the performance of fitted neural Q iteration forreinforcement learning in several partially observable environments, usingthree recurrent neural network architectures: Long Short-Term Memory, GatedRecurrent Unit and MUT1, a recurrent neural architecture evolved from a pool ofseveral thousands candidate architectures. A variant of fitted Q iteration,based on Advantage values instead of Q values, is also explored. The resultsshow that GRU performs significantly better than LSTM and MUT1 for most of theproblems considered, requiring less training episodes and less CPU time beforelearning a very good policy. Advantage learning also tends to produce betterresults.

An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments