Generating Ensembles of Search Policies to Solve Baba is You Levels

Baba Is You is a challenging puzzle game that requires agents to not only navigate spatial environments but also dynamically manipulate the game's rules. In this work, we extend prior evolutionary approaches to solving Baba Is You levels by introducing tree-based search policies evolved through genetic programming. These policies leverage a rich set of base heuristics to guide search more effectively than linear combinations used in earlier work. To improve generalization, we construct a minimal set of reusable search policies through integer linear programming and develop ensemble agents that select appropriate policies for new levels using learned classifiers. Our experiments show that tree-based policies outperform linear ones in terms of level coverage, and that nearest neighbor classifiers generalize well to previously unseen levels.