About

Cedric Langbort is a Professor of Aerospace Engineering at the University of Illinois Urbana-Champaign, with academic positions since June 2020. He holds a Ph.D. in Theoretical & Applied Mechanics from Cornell University (2005), along with master's degrees in Turbulence & Dynamical Systems from INLN/Universite de Nice (2000), and Control Theory from ENSAE/Supaero (1999), where he also earned an Engineering Degree in Aerospace Engineering. His research interests encompass control theory, optimization, and the design of distributed systems, with particular focus on aerospace information technology, transportation cyber-physical systems, and control over networked and large-scale infrastructures. His work includes algorithm fairness and transparency, robust and secure control, and applications to air-traffic control, multi-agent systems, and cyber-security. Langbort has contributed to various research areas such as controls, dynamical systems, estimation, and game theory, with numerous publications in these fields.

Research topics

• Computer Science
• Artificial Intelligence
• Microeconomics
• Mathematics
• Economics
• Computer Security
• Machine Learning
• Algorithm
• Statistics
• Distributed computing
• Mathematical optimization
• Computer network
• Aerospace engineering
• Engineering
• Marketing
• Geography
• Operations research
• Business

Selected publications

• Steady-state Based Approach to Online Non-stochastic Control

  arXiv (Cornell University) · 2026-04-20

  articleOpen accessSenior author

  We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of minimizing the total cost incurred. A recent line of literature in ONC develops algorithms that enjoy sublinear regret with respect to a benchmark based on the set of steady-states that are attainable by a constant input. In this work, we extend this research direction by giving an algorithm that enjoys $\mathcal{O}(\sqrt{T})$ regret with respect to a richer benchmark set, namely the set of steady-states attainable under an \emph{affine controller}. Since this benchmark substantially broadens the comparison class, it provides significantly stronger performance guarantees. Our proposed algorithm combines a Follow-The-Perturbed-Leader-style online non-convex optimization approach with a batching method that maintains stability despite changing policies. Although our proposed algorithm requires solving non-convex subproblems, we show that an approximate solution to this subproblem is sufficient to ensure $\mathcal{O}(\sqrt{T})$ regret. Furthermore, numerical experiments show that our algorithm enjoys lower total cost and similar computation to existing methods in certain settings.

  PublisherOA PDF

• Live LTL Progress Tracking: Towards Task-Based Exploration

  arXiv (Cornell University) · 2026-04-18

  articleOpen access

  Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the progress of autonomous agents through complex, multi-stage tasks. Given a specification in finite linear temporal logic (LTL), the framework establishes a 'tracking vector' which updates at each time step in a trajectory rollout. The values of the vector represent the status of the specification as the trajectory develops, assigning true, false, or 'open' labels (where 'open' is used for indeterminate cases). Applied to an LTL formula tree, the tracking vector can be used to encode detailed information about how a task is executed over a trajectory, providing a potential tool for new performance metrics, diverse exploration, and reward shaping. In this paper, we formally present the framework and algorithm, collectively named Live LTL Progress Tracking, give a simple working example, and demonstrate avenues for its integration into RL models. Future work will apply the framework to problems such as task-space exploration and diverse solution-finding in RL.

  PublisherOA PDF

• Live LTL Progress Tracking: Towards Task-Based Exploration

  arXiv (Cornell University) · 2026-04-18

  preprintOpen access

  Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the progress of autonomous agents through complex, multi-stage tasks. Given a specification in finite linear temporal logic (LTL), the framework establishes a 'tracking vector' which updates at each time step in a trajectory rollout. The values of the vector represent the status of the specification as the trajectory develops, assigning true, false, or 'open' labels (where 'open' is used for indeterminate cases). Applied to an LTL formula tree, the tracking vector can be used to encode detailed information about how a task is executed over a trajectory, providing a potential tool for new performance metrics, diverse exploration, and reward shaping. In this paper, we formally present the framework and algorithm, collectively named Live LTL Progress Tracking, give a simple working example, and demonstrate avenues for its integration into RL models. Future work will apply the framework to problems such as task-space exploration and diverse solution-finding in RL.

  PublisherDOI

• Steady-state Based Approach to Online Non-stochastic Control

  arXiv (Cornell University) · 2026-04-20

  preprintOpen accessSenior author

  We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of minimizing the total cost incurred. A recent line of literature in ONC develops algorithms that enjoy sublinear regret with respect to a benchmark based on the set of steady-states that are attainable by a constant input. In this work, we extend this research direction by giving an algorithm that enjoys $\mathcal{O}(\sqrt{T})$ regret with respect to a richer benchmark set, namely the set of steady-states attainable under an \emph{affine controller}. Since this benchmark substantially broadens the comparison class, it provides significantly stronger performance guarantees. Our proposed algorithm combines a Follow-The-Perturbed-Leader-style online non-convex optimization approach with a batching method that maintains stability despite changing policies. Although our proposed algorithm requires solving non-convex subproblems, we show that an approximate solution to this subproblem is sufficient to ensure $\mathcal{O}(\sqrt{T})$ regret. Furthermore, numerical experiments show that our algorithm enjoys lower total cost and similar computation to existing methods in certain settings.

  PublisherDOI

• "What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)

  ArXiv.org · 2025-06-11

  preprintOpen accessSenior author

  In this work, we provide an extended discussion of a new approach to explainable Reinforcement Learning called Diverse Near-Optimal Alternatives (DNA), first proposed at L4DC 2025. DNA seeks a set of reasonable "options" for trajectory-planning agents, optimizing policies to produce qualitatively diverse trajectories in Euclidean space. In the spirit of explainability, these distinct policies are used to "explain" an agent's options in terms of available trajectory shapes from which a human user may choose. In particular, DNA applies to value function-based policies on Markov decision processes where agents are limited to continuous trajectories. Here, we describe DNA, which uses reward shaping in local, modified Q-learning problems to solve for distinct policies with guaranteed epsilon-optimality. We show that it successfully returns qualitatively different policies that constitute meaningfully different "options" in simulation, including a brief comparison to related approaches in the stochastic optimization field of Quality Diversity. Beyond the explanatory motivation, this work opens new possibilities for exploration and adaptive planning in RL.

  PublisherOA PDFDOI

• Private policies are not optimal in network routing games

  2025-12-09

  articleSenior author

  In the context of network routing games, the "revelation principle," a cornerstone of information design, suggests that messages can take the form of itinerary recommendations and that the probability distribution underlying these recommendations is contingent on the network’s state. This dependency is usually tacitly assumed deterministic. Our study contends that this neglects the possibility that the probability distributions are mere stochastic functions of the state, and illustrates, via a minimal example of an incomplete information nonatomic routing game, how this oversight leads to suboptimal congestion mitigation.

  PublisherDOI

• Revisiting Regret Benchmarks in Online Non-Stochastic Control

  2025-12-09

  articleSenior author

  In the online non-stochastic control problem, an agent sequentially selects control inputs for a linear dynamical system when facing unknown and adversarially selected convex costs and disturbances. A common metric for evaluating control policies in this setting is policy regret, defined relative to the best-in-hindsight linear feedback controller. However, for general convex costs, this benchmark may be less meaningful since linear controllers can be highly suboptimal. To address this, we introduce an alternative, more suitable benchmark—the performance of the best fixed input. We show that this benchmark can be viewed as a natural extension of the standard benchmark used in online convex optimization and propose a novel online control algorithm that achieves sublinear regret with respect to this new benchmark. We also discuss the connections between our method and the original one proposed by Agarwal et al. in their seminal work introducing the online non-stochastic control problem, and compare the performance of both approaches through numerical simulations.

  PublisherDOI

• Revisiting Regret Benchmarks in Online Non-Stochastic Control

  ArXiv.org · 2025-04-23

  preprintOpen accessSenior author

  In the online non-stochastic control problem, an agent sequentially selects control inputs for a linear dynamical system when facing unknown and adversarially selected convex costs and disturbances. A common metric for evaluating control policies in this setting is policy regret, defined relative to the best-in-hindsight linear feedback controller. However, for general convex costs, this benchmark may be less meaningful since linear controllers can be highly suboptimal. To address this, we introduce an alternative, more suitable benchmark--the performance of the best fixed input. We show that this benchmark can be viewed as a natural extension of the standard benchmark used in online convex optimization and propose a novel online control algorithm that achieves sublinear regret with respect to this new benchmark. We also discuss the connections between our method and the original one proposed by Agarwal et al. in their seminal work introducing the online non-stochastic control problem, and compare the performance of both approaches through numerical simulations.

  PublisherOA PDFDOI

• Almost-Bayesian Quadratic Persuasion

  IEEE Transactions on Automatic Control · 2025-01-06

  articleSenior author

  In this article, we relax the Bayesianity assumption in the now-traditional model of Bayesian Persuasion introduced by Kamenica & Gentzkow. Unlike preexisting approaches—which have tackled the possibility of the receiver (Bob) being non-Bayesian by considering that his thought process is not Bayesian yet known to the sender (Alice), possibly up to a parameter—we let Alice merely assume that Bob behaves ‘almost like’ a Bayesian agent, in some sense, without resorting to any specific model. Under this assumption, we study Alice's strategy when both utilities are quadratic and the prior is isotropic. We show that, contrary to the Bayesian case, Alice's optimal response may not be linear anymore. This fact is unfortunate as linear policies remain the only ones for which the induced belief distribution is known. What is more, evaluating linear policies proves difficult except in particular cases, let alone finding an optimal one. Nonetheless, we derive bounds that prove linear policies are near-optimal and allow Alice to compute a near-optimal linear policy numerically. With this solution in hand, we show that Alice shares less information with Bob as he departs more from Bayesianity, much to his detriment.

  PublisherDOI

• On Network Congestion Reduction Using Public Signals Under Boundedly Rational User Equilibria

  IFAC-PapersOnLine · 2024-01-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Boundedly Rational User Equilibria (BRUE) capture situations where all agents on a transportation network are electing the fastest option up to some time indifference, and serve as a relaxation of User Equilibria (UE), where each agent exactly minimizes their travel time. We study how the social cost under BRUE departs from that of UE in the context of static demand and stochastic costs, along with the implications of BRUE on the optimal signaling scheme of a benevolent central planner. We show that the average excess time is sublinear in the maximum time indifference of the agents, though such aggregate may hide disparity between populations and the sublinearity constant depends on the topology of the network. Regarding the design of public signals, even though in the limit where agents are totally indifferent, it is optimal to not reveal any information, there is in general no trend in how much information is optimally disclosed to agents. What is more, an increase in information disclosed may either harm or benefit agents as a whole.

  PublisherDOI

Recent grants

• TWC SBE: Small: From Threat to Boon: Understanding and Controlling Strategic Information Transmission in Cyber-Socio-Physical Systems

  NSF · $500k · 2016–2020

• CIF: Small: Strategic Information Provision for the Control of Cyber-Socio-Physical Transportation Networks

  NSF · $199k · 2020–2023

• CAREER: A dynamic game theoretic approach to cyber-security of controlled systems

  NSF · $400k · 2012–2018

• Distributed Control of Constrained Compartmental Systems, with Applications to Large-Scale Infrastructures

  NSF · $210k · 2008–2012

• EAGER: Extensible Linear Distributed Controllers for Large Multi-Input-Multi-Output Systems, with Applications to Control of Networked Printers

  NSF · $150k · 2010–2013

Frequent coauthors

• Tamer Başar

  44 shared

• Valery Ugrinovskii

  UNSW Canberra

  21 shared

• Emrah Akyol

  Binghamton University

  20 shared

• Farhad Farokhi

  16 shared

• Abhishek Gupta

  15 shared

• Takashi Tanaka

  The University of Texas at Austin

  15 shared

• Jafar Abbaszadeh Chekan

  University of Illinois Urbana-Champaign

  12 shared

• Raffaello D’Andrea

  Cornell University

  11 shared

Labs

• Combustion, Flow, and Plasma Interaction LaboratoryPI

Awards & honors

• Alumni Awards and Endowments
• Alumni Loyalty Award