QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps
arXiv:2409.06888v5 Announce Type: cross Abstract: We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to automatically evaluate Multi-Agent Path Finding (MAPF) algorithms by generating diverse maps. Previously, researchers typically evaluate MAPF algorithms on a set of specific, human-designed maps at their initial stage of algorithm design. However, such fixed maps may not cover all scenarios, and algorithms may overfit to the small set of maps. To seek further improvements, systematic evaluations on a diverse suite of maps are needed. In this work, we propose Quality-Diversity Multi-Agent Path Finding Performance EvaluatoR (QD-MAPPER), a general framework that takes advantage of the QD algorithm to comprehensively understand the performance of MAPF algorithms by generating maps with patterns, be able to make fair comparisons between two MAPF algorithms, providing further information on the selection between two algorithms and on the design of the algorith
arXiv:2409.06888v5 Announce Type: cross Abstract: We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to automatically evaluate Multi-Agent Path Finding (MAPF) algorithms by generating diverse maps. Previously, researchers typically evaluate MAPF algorithms on a set of specific, human-designed maps at their initial stage of algorithm design. However, such fixed maps may not cover all scenarios, and algorithms may overfit to the small set of maps. To seek further improvements, systematic evaluations on a diverse suite of maps are needed. In this work, we propose Quality-Diversity Multi-Agent Path Finding Performance EvaluatoR (QD-MAPPER), a general framework that takes advantage of the QD algorithm to comprehensively understand the performance of MAPF algorithms by generating maps with patterns, be able to make fair comparisons between two MAPF algorithms, providing further information on the selection between two algorithms and on the design of the algorithms. Empirically, we employ this technique to evaluate and compare the behavior of different types of MAPF algorithms, including search-based, priority-based, rule-based, and learning-based algorithms. Through both single-algorithm experiments and comparisons between algorithms, researchers can identify patterns that each MAPF algorithm excels and detect disparities in runtime or success rates between different algorithms.
Executive Summary
This article presents QD-MAPPER, a Quality Diversity framework designed to evaluate Multi-Agent Path Finding (MAPF) algorithms on diverse maps. The authors use Neural Cellular Automata to generate maps with varying patterns, enabling fair comparisons between different MAPF algorithms. The proposed framework is applied to various types of MAPF algorithms, including search-based, priority-based, rule-based, and learning-based algorithms. The results demonstrate the effectiveness of QD-MAPPER in identifying patterns and disparities between algorithms. This approach has significant implications for the development and improvement of MAPF algorithms, as it provides a systematic and comprehensive evaluation method.
Key Points
- ▸ QD-MAPPER uses the Quality Diversity algorithm with Neural Cellular Automata to generate diverse maps for MAPF algorithm evaluation.
- ▸ The framework enables fair comparisons between different MAPF algorithms and identifies patterns and disparities between algorithms.
- ▸ QD-MAPPER is applied to various types of MAPF algorithms, including search-based, priority-based, rule-based, and learning-based algorithms.
Merits
Comprehensive Evaluation Method
QD-MAPPER provides a systematic and comprehensive evaluation method for MAPF algorithms, enabling researchers to identify patterns and disparities between algorithms.
Flexibility and Scalability
The framework's ability to generate diverse maps with varying patterns makes it flexible and scalable for evaluating different MAPF algorithms.
Improved Algorithm Design
QD-MAPPER's evaluation method can inform the design of MAPF algorithms, leading to improved performance and efficiency.
Demerits
Computational Complexity
Generating diverse maps with Neural Cellular Automata may increase computational complexity, potentially limiting the framework's applicability in certain scenarios.
Limited Generalizability
The effectiveness of QD-MAPPER may be limited to specific types of MAPF algorithms or problem domains, requiring further research to establish its generalizability.
Expert Commentary
The QD-MAPPER framework presents a significant advancement in the evaluation of MAPF algorithms, offering a comprehensive and systematic approach to identifying patterns and disparities between algorithms. The use of Neural Cellular Automata to generate diverse maps is a novel and effective method, enabling fair comparisons between different MAPF algorithms. However, the computational complexity and limited generalizability of the framework require further research to establish its applicability in various scenarios. Nonetheless, QD-MAPPER's evaluation method has the potential to inform the design of MAPF algorithms, leading to improved performance and efficiency in various applications.
Recommendations
- ✓ Further research is needed to establish the generalizability of QD-MAPPER and its applicability in various scenarios.
- ✓ The development of more efficient and scalable methods for generating diverse maps with Neural Cellular Automata could enhance the framework's computational complexity.