2023/07/10 by Rubens O. Moraes, David S. Aleixo, Moraes, Rubens O. +5 · 1 citation
Computer Science · Economics, Econometrics and Finance · Psychology · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Educational Games and Gamification #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.2307.04893
openalex publication_date 2023/07/10 · openalex created_date 2023/07/13 · openalex updated_date 2026/07/28
This paper introduces Local Learner (2L), an algorithm for providing a set of reference strategies to guide the search for programmatic strategies in two-player zero-sum games. Previous learning algorithms, such as Iterated Best Response (IBR), Fictitious Play (FP), and Double-Oracle (DO), can be computationally expensive or miss important information for guiding search algorithms. 2L actively selects a set of reference strategies to improve the search signal. We empirically demonstrate the advantages of our approach while guiding a local search algorithm for synthesizing strategies in three games, including MicroRTS, a challenging real-time strategy game. Results show that 2L learns reference strategies that provide a stronger search signal than IBR, FP, and DO. We also simulate a tournament of MicroRTS, where a synthesizer using 2L outperformed the winners of the two latest MicroRTS competitions, which were programmatic strategies written by human programmers.