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

Sample efficiency is a crucial problem in deep reinforcement learning. Recentalgorithms, such as REDQ and DroQ, found a way to improve the sample efficiencyby increasing the update-to-data (UTD) ratio to 20 gradient update steps on thecritic per environment sample. However, this comes at the expense of a greatlyincreased computational cost. To reduce this computational burden, we introduceCross$Q$: a lightweight algorithm that makes careful use of Batch Normalizationand removes target networks to surpass the state-of-the-art in sampleefficiency while maintaining a low UTD ratio of $1$. Notably, Cross$Q$ does notrely on advanced bias-reduction schemes used in current methods. Cross$Q$'scontributions are thus threefold: (1) state-of-the-art sample efficiency, (2)substantial reduction in computational cost compared to REDQ and DroQ, and (3)ease of implementation, requiring just a few lines of code on top of SAC.

CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity