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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In order for reinforcement learning techniques to be useful in real-worlddecision making processes, they must be able to produce robust performance fromlimited data. Deep policy optimization methods have achieved impressive resultson complex tasks, but their real-world adoption remains limited because theyoften require significant amounts of data to succeed. When combined with smallsample sizes, these methods can result in unstable learning due to theirreliance on high-dimensional sample-based estimates. In this work, we developtechniques to control the uncertainty introduced by these estimates. Weleverage these techniques to propose a deep policy optimization approachdesigned to produce stable performance even when data is scarce. The resultingalgorithm, Uncertainty-Aware Trust Region Policy Optimization, generates robustpolicy updates that adapt to the level of uncertainty present throughout thelearning process.

Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach