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

A key challenge in science and engineering is to design experiments to learnabout some unknown quantity of interest. Classical experimental designoptimally allocates the experimental budget to maximize a notion of utility(e.g., reduction in uncertainty about the unknown quantity). We consider a richsetting, where the experiments are associated with states in a {\em Markovchain}, and we can only choose them by selecting a {\em policy} controlling thestate transitions. This problem captures important applications, fromexploration in reinforcement learning to spatial monitoring tasks. We proposean algorithm -- \textsc{markov-design} -- that efficiently selects policieswhose measurement allocation \emph{provably converges to the optimal one}. Thealgorithm is sequential in nature, adapting its choice of policies(experiments) informed by past measurements. In addition to our theoreticalanalysis, we showcase our framework on applications in ecological surveillanceand pharmacology.

Active Exploration via Experiment Design in Markov Chains