Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting
Authors: Ren-Jian Wang, Ke Xue, Haopu Shang, Chao Qian, Haobo Fu, Qiang Fu
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on synthetic functions and several complex tasks (i.e., QDGym, robotic arm, and Mario environment generation), showing that NSS achieves better performance than not only other MO-based selection methods but also state-of-the-art selection methods in QD. |
| Researcher Affiliation | Collaboration | 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Tencent AI Lab, Shenzhen, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Non-surrounded-dominated Sorting |
| Open Source Code | Yes | Our code is available at https://github.com/lamda-bbo/NSS. |
| Open Datasets | Yes | We conduct experiments on four different environments, i.e., QD Hopper, Walker, Half Cheetah, and Ant. These tasks aim to generate a set of policies that move forward as fast as possible and are diverse in the frequency of feet use. Thus, the objective function is determined by the agent s forward speed, and the behavior descriptor functions are defined as the fraction of time each foot was touching the ground during an episode. |
| Dataset Splits | No | The paper discusses the iterative process of QD algorithms and performance metrics like QD-Score, but it does not specify any training, validation, or test dataset splits in the context of standard supervised learning benchmarks. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments, such as GPU models, CPU types, or memory specifications. It only discusses running time. |
| Software Dependencies | No | The paper mentions various algorithms and frameworks (e.g., ME, PGA-ME, OG-ME) but does not list any specific software dependencies or their version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | The detailed settings of experiments are provided in Appendix B. |