Emergent Behaviors in Mixed-Autonomy Traffic. - Cathy Wu 002; Aboudy Kreidieh; Eugene Vinitsky; Alexandre M. Bayen (CoRL, 2017)

Framework for control and deep reinforcement learning in traffic. - Cathy Wu 002; Kanaad Parvate; Nishant Kheterpal; Leah Dickstein; Ankur Mehta; Eugene Vinitsky; Alexandre M. Bayen (ITSC, 2017)

Multi-lane reduction: A stochastic single-lane model for lane changing. - Cathy Wu 002; Eugene Vinitsky; Aboudy Kreidieh; Alexandre M. Bayen (ITSC, 2017)

Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control. - Cathy Wu 002; Aboudy Kreidieh; Kanaad Parvate; Eugene Vinitsky; Alexandre M. Bayen (CoRR, 2017)

Benchmarks for reinforcement learning in mixed-autonomy traffic. - Eugene Vinitsky; Aboudy Kreidieh; Luc Le Flem; Nishant Kheterpal; Kathy Jang; Cathy Wu 002; Fangyu Wu 003; Richard Liaw; Eric Liang; Alexandre M. Bayen (CoRL, 2018)

Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion. - Eugene Vinitsky; Kanaad Parvate; Aboudy Kreidieh; Cathy Wu 002; Alexandre M. Bayen (ITSC, 2018)

Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles. - Kathy Jang; Eugene Vinitsky; Behdad Chalaki; Ben Remer; Logan Beaver; Andreas A. Malikopoulos; Alexandre M. Bayen (ICCPS, 2019)

Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning. - Behdad Chalaki; Logan E. Beaver; Ben Remer; Kathy Jang; Eugene Vinitsky; Alexandre M. Bayen; Andreas A. Malikopoulos (ICCA, 2020)

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. - Michael Dennis 001; Natasha Jaques; Eugene Vinitsky; Alexandre M. Bayen; Stuart Russell 001; Andrew Critch; Sergey Levine (NeurIPS, 2020)

Robust Reinforcement Learning using Adversarial Populations. - Eugene Vinitsky; Yuqing Du; Kanaad Parvate; Kathy Jang; Pieter Abbeel; Alexandre M. Bayen (CoRR, 2020)

Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL. - Eugene Vinitsky; Nathan Lichtle; Kanaad Parvate; Alexandre M. Bayen (CoRR, 2020)

Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control. - Nathan Lichtlé; Eugene Vinitsky; George Gunter; Akash Velu; Alexandre M. Bayen (ITSC, 2021)

The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games. - Chao Yu 005; Akash Velu; Eugene Vinitsky; Yu Wang 002; Alexandre M. Bayen; Yi Wu 013 (CoRR, 2021)

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers. - Jonathan W. Lee; George Gunter; Rabie Ramadan 002; Sulaiman Almatrudi; Paige Arnold; John Aquino; William Barbour; Rahul Bhadani; Joy Carpio; Fang-Chieh Chou; Marsalis T. Gibson; Xiaoqian Gong; et al. (CoRR, 2021)

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings. - Eugene Vinitsky; Raphael Köster; John P. Agapiou; Edgar A. Duéñez-Guzmán; Alexander Sasha Vezhnevets; Joel Z. Leibo (CoRR, 2021)

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data. - Nathan Lichtlé; Eugene Vinitsky; Matthew Nice; Benjamin Seibold; Dan Work; Alexandre M. Bayen (ICRA, 2022)

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 N. Foerster (NeurIPS, 2022)

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games. - Chao Yu 005; Akash Velu; Eugene Vinitsky; Jiaxuan Gao; Yu Wang 002; Alexandre M. Bayen; Yi Wu 013 (NeurIPS, 2022)

On the approximability of Time Disjoint Walks. - Alexandre M. Bayen; Jesse Goodman; Eugene Vinitsky (J. Comb. Optim., 2022)

Flow: A Modular Learning Framework for Mixed Autonomy Traffic. - Cathy Wu 002; Abdul Rahman Kreidieh; Kanaad Parvate; Eugene Vinitsky; Alexandre M. Bayen (IEEE Trans. Robotics, 2022)

From sim-to-real: learning and deploying autonomous vehicle controllers that improve transportation metrics - Eugene Vinitsky (2022)

Stabilizing Unsupervised Environment Design with a Learned Adversary. - Ishita Mediratta; Minqi Jiang; Jack Parker-Holder; Michael Dennis 001; Eugene Vinitsky; Tim Rocktäschel (CoLLAs, 2023)

Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data. - Nathan Lichtlé; Kathy Jang; Adit Shah; Eugene Vinitsky; Jonathan W. Lee; Alexandre M. Bayen (ITSC, 2023)

Unified Automatic Control of Vehicular Systems With Reinforcement Learning. - Zhongxia Yan 001; Abdul Rahman Kreidieh; Eugene Vinitsky; Alexandre M. Bayen; Cathy Wu 002 (IEEE Trans Autom. Sci. Eng., 2023)

Optimizing Mixed Autonomy Traffic Flow with Decentralized Autonomous Vehicles and Multi-Agent Reinforcement Learning. - Eugene Vinitsky; Nathan Lichtlé; Kanaad Parvate; Alexandre M. Bayen (ACM Trans. Cyber Phys. Syst., 2023)

Human-compatible driving agents through data-regularized self-play reinforcement learning. - Daphne Cornelisse; Eugene Vinitsky (RLJ, 2024)

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs. - Jonathan W. Lee; Han Wang 024; Kathy Jang; Amaury Hayat; Matthew R. Bunting; Arwa Alanqary; William Barbour; Zhe Fu; Xiaoqian Gong; George Gunter; Sharon Hornstein; Abdul Rahman Kreidieh; et al. (CoRR, 2024)

Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test. - Kathy Jang; Nathan Lichtlé; Eugene Vinitsky; Adit Shah; Matthew R. Bunting; Matthew Nice; Benedetto Piccoli; Benjamin Seibold; Daniel B. Work; Maria Laura Delle Monache; Jonathan Sprinkle; Jonathan W. Lee; et al. (CoRR, 2024)

Human-compatible driving partners through data-regularized self-play reinforcement learning. - Daphne Cornelisse; Eugene Vinitsky (CoRR, 2024)

Few-shot In-Context Preference Learning Using Large Language Models. - Chao Yu 005; Hong Lu; Jiaxuan Gao; Qixin Tan; Xinting Yang; Yu Wang 002; Yi Wu 013; Eugene Vinitsky (CoRR, 2024)

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers. - Nathan Lichtlé; Eugene Vinitsky; Matthew Nice; Rahul Bhadani; Matthew R. Bunting; Fangyu Wu 003; Benedetto Piccoli; Benjamin Seibold; Daniel B. Work; Jonathan W. Lee; Jonathan Sprinkle; Alexandre M. Bayen (IEEE Trans. Robotics, 2024)

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS. - Saman Kazemkhani; Aarav Pandya; Daphne Cornelisse; Brennan Shacklett; Eugene Vinitsky (ICLR, 2025)

Robust Autonomy Emerges from Self-Play. - Marco F. Cusumano-Towner; David Hafner; Alexander Hertzberg; Brody Huval; Aleksei Petrenko; Eugene Vinitsky; Erik Wijmans; Taylor Killian; Stuart Bowers; Ozan Sener; Philipp Krähenbühl; Vladlen Koltun (CoRR, 2025)

Reevaluating Policy Gradient Methods for Imperfect-Information Games. - Max Rudolph; Nathan Lichtle; Sobhan Mohammadpour; Alexandre M. Bayen; J. Zico Kolter; Amy Zhang 001; Gabriele Farina; Eugene Vinitsky; Samuel Sokota (CoRR, 2025)

Building reliable sim driving agents by scaling self-play. - Daphne Cornelisse; Aarav Pandya; Kevin Joseph; Joseph Suárez; Eugene Vinitsky (CoRR, 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 (CoRR, 2025)