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 algorithms have had tremendous successes in onlinelearning settings. However, these successes have relied on low-stakesinteractions between the algorithmic agent and its environment. In manysettings where RL could be of use, such as health care and autonomous driving,the mistakes made by most online RL algorithms during early training come withunacceptable costs. These settings require developing reinforcement learningalgorithms that can operate in the so-called batch setting, where thealgorithms must learn from set of data that is fixed, finite, and generatedfrom some (possibly unknown) policy. Evaluating policies different from the onethat collected the data is called off-policy evaluation, and naturally posescounter-factual questions. In this project we show how off-policy evaluationand the estimation of treatment effects in causal inference are two approachesto the same problem, and compare recent progress in these two areas.

Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains