Choosing Well Your Opponents: How to Guide the Synthesis of Programmatic Strategies
This addresses computational inefficiencies in strategy synthesis for game AI, offering a domain-specific improvement.
The paper tackles the problem of guiding search for programmatic strategies in two-player zero-sum games by introducing the Local Learner (2L) algorithm, which actively selects reference strategies to improve search signals, resulting in outperforming human-written strategies in a MicroRTS tournament.
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.