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

Online game playing algorithms produce high-quality strategies with afraction of memory and computation required by their offline alternatives.Continual Resolving (CR) is a recent theoretically sound approach to onlinegame playing that has been used to outperform human professionals in poker.However, parts of the algorithm were specific to poker, which enjoys manyproperties not shared by other imperfect information games. We present adomain-independent formulation of CR applicable to any two-player zero-sumextensive-form games that works with an abstract resolving algorithm. Wefurther describe and implement its Monte Carlo variant (MCCR) which uses MonteCarlo Counterfactual Regret Minimization (MCCFR) as a resolver. We prove thecorrectness of CR and show an $O(T^{-1/2})$-dependence of MCCR's exploitabilityon the computation time. Furthermore, we present an empirical comparison ofMCCR with incremental tree building to Online Outcome Sampling andInformation-set MCTS on several domains.

Monte Carlo Continual Resolving for Online Strategy Computation in Imperfect Information Games