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20162025
most citedConstrained Reinforcement Learning Has Zero Duality Gap

10 citations · 16 across the 7 of their papers we have counts for

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6 papers · 1 filter

math.OC2025

The Lagrangian Method for Solving Constrained Markov Games

Soham Das, Santiago Paternain, Luiz F. O. Chamon +1

We propose the concept of a Lagrangian game to solve constrained Markov games. Such games model scenarios where agents face cost constraints in addition to their individual rewards…

math.OC2020

Resilient Control: Compromising to Adapt

Luiz F. O. Chamon, Alexandre Amice, Santiago Paternain +1

In optimal control problems, disturbances are typically dealt with using robust solutions, such as H-infinity or tube model predictive control, that plan control actions feasible f…

math.OC2020

Counterfactual Programming for Optimal Control

Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro

In recent years, considerable work has been done to tackle the issue of designing control laws based on observations to allow unknown dynamical systems to perform pre-specified tas…

math.OC2019

Source Seeking in Unknown Environments with Convex Obstacles

Bruno A. Angélico, Luiz F. O. Chamon, Santiago Paternain +2

Navigation tasks often cannot be defined in terms of a target, either because global position information is unavailable or unreliable or because target location is not explicitly…

math.OC2019

Distributed Constrained Online Learning

Santiago Paternain, Soomin Lee, Michael M. Zavlanos +1

In this paper, we consider groups of agents in a network that select actions in order to satisfy a set of constraints that vary arbitrarily over time and minimize a time-varying fu…

math.OC2016

Online Learning of Feasible Strategies in Unknown Environments

Santiago Paternain, Alejandro Ribeiro

Define an environment as a set of convex constraint functions that vary arbitrarily over time and consider a cost function that is also convex and arbitrarily varying. Agents that…