QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps

Researchers developed QD-MAPPER, a framework using Quality Diversity algorithms and Neural Cellular Automata to automatically generate diverse maps for evaluating Multi-Agent Path Finding (MAPF) algorithms. This addresses the limitation of testing MAPF algorithms on fixed, human-designed maps that may not cover all scenarios and could lead to overfitting.

• →QD-MAPPER uses Quality Diversity algorithms with Neural Cellular Automata to generate diverse test maps for MAPF algorithm evaluation.
• →Traditional MAPF testing relies on fixed human-designed maps which may cause algorithms to overfit to limited scenarios.
• →The framework enables fair comparisons between different MAPF algorithm types including search-based, priority-based, rule-based, and learning-based approaches.
• →Researchers can identify performance patterns and detect disparities in runtime or success rates between different algorithms.
• →The system provides comprehensive algorithmic evaluation by automatically generating maps with diverse patterns and characteristics.