Inter-Level Cooperation in Hierarchical Reinforcement Learning. - Abdul Rahman Kreidieh; Samyak Parajuli; Nathan Lichtle; Yiling You; Rayyan Nasr; Alexandre M. Bayen (CoRR, 2019)

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)

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)

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)

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)

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)

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)

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)

Stabilization and control of dynamical systems: from classical methods to reinforcement learning. (Stabilisation et contrôle des systèmes dynamiques : des méthodes classiques à l'apprentissage par renforcement). - Nathan Lichtlé (2024)

A Tutorial on Neural Network-Based Solvers for Hyperbolic Conservation Laws: Supervised vs. Unsupervised Learning, and Applications to Traffic Modeling. - Alexi Canesse; Zhe Fu; Nathan Lichtlé; Hossein Nick Zinat Matin; Zihe Liu; Maria Laura Delle Monache; Alexandre M. Bayen (NeuS, 2025)

A novel approach to feedback control with deep reinforcement learning. - Kala Agbo bidi; Jean-Michel Coron; Amaury Hayat; Nathan Lichtlé (Syst. Control. Lett., 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)

(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs. - Nathan Lichtlé; Alexi Canesse; Zhe Fu; Hossein Nick Zinat Matin; Maria Laura Delle Monache; Alexandre M. Bayen (CoRR, 2025)