diff --git a/_data/paperlist.yml b/_data/paperlist.yml index 41e1b59..d986fa1 100644 --- a/_data/paperlist.yml +++ b/_data/paperlist.yml @@ -1,5 +1,25 @@ papers: +- title: "Mix-ME: Quality-Diversity for Multi-Agent Learning" + authors: + - Garðar Ingvarsson + - Mikayel Samvelyan + - Bryan Lim + - Manon Flageat + - Antoine Cully + - Tim Rocktäschel + year: 2023 + pdfurl: "https://arxiv.org/pdf/2311.01829.pdf" + bibtex: | + "@article{ingvarsson2023mixme, + title={Mix-ME: Quality-Diversity for Multi-Agent Learning}, + author={Gar{\dh}ar Ingvarsson and Mikayel Samvelyan and Bryan Lim and Manon Flageat and Antoine Cully and Tim Rockt{\"a}schel}, + journal={arXiv preprint arXiv:2311.01829}, + year={2023} }" + tags: + - robotics + abstract: "In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often required to adapt to varying contexts and requirements. This is the realm of Quality-Diversity (QD), which aims to discover a collection of high-performing solutions, each with their own unique characteristics. QD methods have recently seen success in many domains, including robotics, where they have been used to discover damage-adaptive locomotion controllers. However, most existing work has focused on single-agent settings, despite many tasks of interest being multi-agent. To this end, we introduce Mix-ME, a novel multi-agent variant of the popular MAP-Elites algorithm that forms new solutions using a crossover-like operator by mixing together agents from different teams. We evaluate the proposed methods on a variety of partially observable continuous control tasks. Our evaluation shows that these multi-agent variants obtained by Mix-ME not only compete with single-agent baselines but also often outperform them in multi-agent settings under partial observability." + - title: "Diversity Preservation in Minimal Criterion Coevolution through Resource Limitation." authors: - Jonathan C. Brant