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

We introduce a sampling perspective to tackle the challenging task oftraining robust Reinforcement Learning (RL) agents. Leveraging the powerfulStochastic Gradient Langevin Dynamics, we present a novel, scalable two-playerRL algorithm, which is a sampling variant of the two-player policy gradientmethod. Our algorithm consistently outperforms existing baselines, in terms ofgeneralization across different training and testing conditions, on severalMuJoCo environments. Our experiments also show that, even for objectivefunctions that entirely ignore potential environmental shifts, our samplingapproach remains highly robust in comparison to standard RL algorithms.

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics