From 2826f2b4eed0c3b9b4b1c0f4d2495f8804b017e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Gar=C3=B0ar=20Ingvarsson?= <gardarjuto@gmail.com>
Date: Tue, 30 Jan 2024 11:58:59 +0000
Subject: [PATCH 1/2] add Mix-ME paper

---
 _data/paperlist.yml | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/_data/paperlist.yml b/_data/paperlist.yml
index 41e1b59..67a4858 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

From 8e768ac94ce15368ac6061a1b41c9a9b2df118ef Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Gar=C3=B0ar=20Ingvarsson?= <gardarjuto@gmail.com>
Date: Tue, 30 Jan 2024 12:01:47 +0000
Subject: [PATCH 2/2] Fix title

---
 _data/paperlist.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_data/paperlist.yml b/_data/paperlist.yml
index 67a4858..d986fa1 100644
--- a/_data/paperlist.yml
+++ b/_data/paperlist.yml
@@ -1,6 +1,6 @@
 papers:
 
-- title: "Mix-Me: Quality-Diversity for Multi-Agent Learning"
+- title: "Mix-ME: Quality-Diversity for Multi-Agent Learning"
   authors:
   - Garðar Ingvarsson
   - Mikayel Samvelyan