  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.
