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 introduce a value-based RL agent, which we call BBF, that achievessuper-human performance in the Atari 100K benchmark. BBF relies on scaling theneural networks used for value estimation, as well as a number of other designchoices that enable this scaling in a sample-efficient manner. We conductextensive analyses of these design choices and provide insights for futurework. We end with a discussion about updating the goalposts forsample-efficient RL research on the ALE. We make our code and data publiclyavailable athttps://github.com/google-research/google-research/tree/master/bigger_better_faster.

Bigger, Better, Faster: Human-level Atari with human-level efficiency