Portrait of Omar G. Younis

Omar G. Younis

Collaborating researcher
Supervisor
Yoshua Bengio
Research Topics
Foundation Models
Offline Reinforcement Learning
Reinforcement Learning
Robotics
Website
Google Scholar
GitHub

Publications

CUBE: A Standard for Unifying Agent Benchmarks
Alexandre Lacoste
Nicolas Gontier
Oleh Shliazhko
Aman Jaiswal
Kusha Sareen
Shailesh Nanisetty
Joan Cabezas
Manuel Del Verme
Omar G. Younis
Simone Baratta
Matteo Avalle
Imene Kerboua
Xing Han Lu
Elron Bandel
Michal Shmueli-Scheuer
Asaf Yehudai
Leshem Choshen
Jonathan Lebensold
Sean Hughes
Massimo Caccia … (see 6 more)
Alexandre Drouin
Siva Reddy
Tao Yu
Yu Su
Graham Neubig
Dawn Song
The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires s… (see more)ubstantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.
2026-03-15
arXiv (preprint)
doi.org
arxiv.org
Using Unity to Help Solve Reinforcement Learning
Connor Brennan
Andrew Robert Williams
Omar G. Younis
Vedant Vyas
Daria Yasafova
Irina Rish
Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Uni… (see more)verse — 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.
2024-09-25
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
openreview.net
openreview.net