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

Visual domain randomization in simulated environments is a widely used methodto transfer policies trained in simulation to real robots. However, domainrandomization and augmentation hamper the training of a policy. Asreinforcement learning struggles with a noisy training signal, this additionalnuisance can drastically impede training. For difficult tasks it can evenresult in complete failure to learn. To overcome this problem we propose topre-train a perception encoder that already provides an embedding invariant tothe randomization. We demonstrate that this yields consistently improvedresults on a randomized version of DeepMind control suite tasks and a stackingenvironment on arbitrary backgrounds with zero-shot transfer to a physicalrobot.

Pre-training of Deep RL Agents for Improved Learning under Domain Randomization