VLDB 2026 Research / reviewers in the wild / expert
Daria Yasafova
dblp:318/3061
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Reinforcement learning · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Games and playful interaction · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › reinforcement learning environment
environment design |
0.8 | 1 | 2024 | Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024 |
Machine learning › Reinforcement learning › reinforcement learning environment › environment design
procedural environment generation |
0.8 | 1 | 2024 | Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024 |
Machine learning › Reinforcement learning
reinforcement learning environment |
0.8 | 1 | 2024 | Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
procedural generation · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Using Unity to Help Solve Reinforcement LearningabstractLeveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Universe — an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of XLand 2.0 complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Additionally, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems. Connor Brennan, Andrew Robert Williams, Omar G. Younis, Vedant Vyas, Daria Yasafova, Irina Rish |
NeurIPS | 5 |