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

Reinforcement learning solely from an agent's self-generated data is oftenbelieved to be infeasible for learning on real robots, due to the amount ofdata needed. However, if done right, agents learning from real data can besurprisingly efficient through re-using previously collected sub-optimal data.In this paper we demonstrate how the increased understanding of off-policylearning methods and their embedding in an iterative online/offline scheme(``collect and infer'') can drastically improve data-efficiency by using allthe collected experience, which empowers learning from real robot experienceonly. Moreover, the resulting policy improves significantly over the state ofthe art on a recently proposed real robot manipulation benchmark. Our approachlearns end-to-end, directly from pixels, and does not rely on additional humandomain knowledge such as a simulator or demonstrations.

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots