Evolutionary Optimization of Baba Is You Agents

Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Search algorithm has shown great results in Sokoban, which apart from the rule modification shares many similarities to Baba is You. It uses multiple heuristics to guide the search into promising regions of the search space. In this work, we are proposing a similar concept for solving Baba is You based on multiple heuristics that are aggregated for guiding a tree-based search process. However, finding parameters for weighting/prioritizing the different heuristics is a non-trivial task. This process is done, by applying evolutionary algorithms for single- and multi-objective optimization. Specifically, we compare the effects of these different optimization schemes on the agents' general level-solving capabilities. In all cases, the agent was able to adapt well to the training and test set with no significant differences among the optimization schemes. Compared with state-of-the-art Baba is You agents our search-based approach shows an improved performance in terms of the number of levels being solved, as well as a reduction in the average time required to solve a level.