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

We study networks of communicating learning agents that cooperate to solve acommon nonstochastic bandit problem. Agents use an underlying communicationnetwork to get messages about actions selected by other agents, and dropmessages that took more than $d$ hops to arrive, where $d$ is a delayparameter. We introduce \textsc{Exp3-Coop}, a cooperative version of the {\scExp3} algorithm and prove that with $K$ actions and $N$ agents the averageper-agent regret after $T$ rounds is at most of order $\sqrt{\bigl(d+1 +\tfrac{K}{N}\alpha_{\le d}\bigr)(T\ln K)}$, where $\alpha_{\le d}$ is theindependence number of the $d$-th power of the connected communication graph$G$. We then show that for any connected graph, for $d=\sqrt{K}$ the regretbound is $K^{1/4}\sqrt{T}$, strictly better than the minimax regret $\sqrt{KT}$for noncooperating agents. More informed choices of $d$ lead to bounds whichare arbitrarily close to the full information minimax regret $\sqrt{T\ln K}$when $G$ is dense. When $G$ has sparse components, we show that a variant of\textsc{Exp3-Coop}, allowing agents to choose their parameters according totheir centrality in $G$, strictly improves the regret. Finally, as a by-productof our analysis, we provide the first characterization of the minimax regretfor bandit learning with delay.

Delay and Cooperation in Nonstochastic Bandits