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

This paper introduces Local Learner (2L), an algorithm for providing a set ofreference strategies to guide the search for programmatic strategies intwo-player zero-sum games. Previous learning algorithms, such as Iterated BestResponse (IBR), Fictitious Play (FP), and Double-Oracle (DO), can becomputationally expensive or miss important information for guiding searchalgorithms. 2L actively selects a set of reference strategies to improve thesearch signal. We empirically demonstrate the advantages of our approach whileguiding a local search algorithm for synthesizing strategies in three games,including MicroRTS, a challenging real-time strategy game. Results show that 2Llearns reference strategies that provide a stronger search signal than IBR, FP,and DO. We also simulate a tournament of MicroRTS, where a synthesizer using 2Loutperformed the winners of the two latest MicroRTS competitions, which wereprogrammatic strategies written by human programmers.

Choosing Well Your Opponents: How to Guide the Synthesis of Programmatic Strategies