Nathan Lichtlé — 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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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 ↗

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 Nicolaus Foerster

NeurIPS 2022accept

We introduce \textit{Nocturne}, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference and theory of mind in real-world multi-agent settings without the computational overhead of computer vision an…

Cited by 52 Source ↗PDF ↗code ↗Scholar ↗