Alexandre M Bayen — accepted AI-conference papers

2026

(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

ICLR 2026poster

We introduce (U)NFV, a modular neural network architecture that generalizes classical finite volume (FV) methods for solving hyperbolic conservation laws. Hyperbolic partial differential equations (PDEs) are challenging to solve, particularly conservation laws whose physically relevant solutions con…

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2026

Reevaluating Policy Gradient Methods for Imperfect-Information Games

Max Rudolph, Nathan Lichtlé, Sobhan Mohammadpour, Alexandre M Bayen, J Zico Kolter, Amy Zhang +3

ICLR 2026poster

In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed numerous DRL algorithms based on fictitious play (FP), double oracle (DO), and counterfactual regret minimization (CFR). I…

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2025

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models

Fangyu Wu, Dequan Wang, Minjune Hwang, Chenhui Hao, Jiawei Lu, Jiamu Zhang +3

ICRA 2025

A significant portion of roads, particularly in densely populated developing countries, lacks explicitly defined right-of-way rules. These understructured roads pose substantial challenges for autonomous vehicle motion planning, where efficient and safe navigation relies on understanding decentraliz

Cited by 10 Source ↗Scholar ↗

2024

Modifying Adaptive Cruise Control Systems for String Stable Stop-and -Go Wave Control

Fangyu Wu, Joy Carpio, Matthew R. Bunting, Matthew Nice, Daniel B. Work, Jonathan Sprinkle +3

RA-L 2024

This letter addresses the important issue of energy inefficiency and air pollution resulting from stop-and-go waves on highways by introducing a novel controller called the Attenuative Kerner's Model (AKM). The objective of AKM is to enhance an existing Adaptive Cruise Control (ACC) system to improv

Cited by 4 Source ↗Scholar ↗

2022

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Nathan Lichtlé, Eugene Vinitsky, Matthew Nice, Benjamin Seibold, Dan Work, Alexandre M. Bayen

ICRA 2022poster

Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory d…

Cited by 45 Source ↗code ↗Scholar ↗

2021

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Fang-Chieh Chou, Marsalis Gibson, Rahul Bhadani, Alexandre M. Bayen, Jonathan Sprinkle

ICRA 2021poster

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collec…

Cited by 7 Source ↗Scholar ↗

2018

Benchmarks for reinforcement learning in mixed-autonomy traffic

Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu +4

CoRL 2018

We release new benchmarks in the use of deep reinforcement learning (RL) to create controllers for mixed-autonomy traffic, where connected and autonomous vehicles (CAVs) interact with human drivers and infrastructure. Benchmarks, such as Mujoco or the Arcade Learning Environment, have spurred new re

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2018

Stabilizing Traffic with Autonomous Vehicles

Cathy Wu, Alexandre M. Bayen, Ankur Mehta

ICRA 2018poster

Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematic…

Cited by 185 Source ↗Scholar ↗

2018

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade +2

ICLR 2018oral

Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free…

Cited by 187 Source ↗PDF ↗Scholar ↗

2017

Emergent Behaviors in Mixed-Autonomy Traffic

Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, Alexandre M. Bayen

CoRL 2017

Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understa

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