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

Agents trained with DQN rely on an observation at each timestep to decidewhat action to take next. However, in real world applications observations canchange or be missing entirely. Examples of this could be a light bulb breakingdown, or the wallpaper in a certain room changing. While these situationschange the actual observation, the underlying optimal policy does not change.Because of this we want our agent to continue taking actions until it receivesa (recognized) observation again. To achieve this we introduce a combination ofa neural network architecture that uses hidden representations of theobservations and a novel n-step loss function. Our implementation is able towithstand location based blindness stretches longer than the ones it wastrained on, and therefore shows robustness to temporary blindness. For accessto our implementation, please email Nathan, Marije, or Pau.

Lights out: training RL agents robust to temporary blindness