Daria Yasafova

dblp:318/3061 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › reinforcement learning environment
environment design
0.812024
Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024
Machine learning › Reinforcement learning › reinforcement learning environment › environment design
procedural environment generation
0.812024
Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024
Machine learning › Reinforcement learning
reinforcement learning environment
0.812024
Using Unity to Help Solve Reinforcement Learning · NeurIPS 2024

Methods — techniques the papers use, named apart from their topics

procedural generation · 1.5
YearPublicationVenuePosition
2024 Using Unity to Help Solve Reinforcement Learning
abstract
Leveraging 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
NeurIPS5