Building reliable sim driving agents by scaling self-play

Daphne Cornelisse, Aarav Pandya, Kevin Joseph, Joseph Suárez, Eugene Vinitsky

arXiv, 2025

Reevaluating Policy Gradient Methods for Imperfect-Information Games

Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour, Alexandre Bayen, J. Zico Kolter, Amy Zhang, Gabriele Farina, Eugene Vinitsky, Samuel Sokota

arXiv, 2025

ICPL: Few-shot In-context Preference Learning via LLMs

Chao Yu, Qixin Tan, Hong Lu, Jiaxuan Gao, Xinting Yang, Yu Wang, Yi Wu, Eugene Vinitsky

arXiv, 2024

Robust Autonomy Emerges from Self-Play

Marco Cusumano-Towner, David Hafner, Alex Hertzberg, Brody Huval, Aleksei Petrenko, Eugene Vinitsky, Erik Wijmans, Taylor Killian, Stuart Bowers, Ozan Sener, Philipp Krähenbühl, Vladlen Koltun

International Conference on Machine Learning (ICML), 2025

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS

Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky

International Conference on Learning Representations (ICLR), 2025

Decentralized Aerial Manipulation of a Cable-Suspended Load Using Multi-Agent Reinforcement Learning

Jack Zeng, Andreu Matoses Gimenez, Eugene Vinitsky, Javier Alonso-Mora, Sihao Sun

Conference on Robot Learning (CoRL), 2025

Human-compatible driving partners through data-regularized self-play reinforcement learning

Daphne Cornelisse, Eugene Vinitsky

Reinforcement Learning Journal, 2024

Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world

Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster

NeurIPS, 2022

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu

NeurIPS, 2022

Unified Automatic Control of Vehicular Systems with Reinforcement Learning

Zhongxia Yan, Abdul Rahman Kreidieh, Eugene Vinitsky, Alexandre M. Bayen, Cathy Wu

IEEE Transactions on Automation Science and Engineering, 2022