60 citations · 153 across the 26 of their papers we have counts for
35 papers
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…
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning
Leopoldo Agorio, Sean Van Alen, Santiago Paternain +2
Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to…
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons
Weiqin Chen, Santiago Paternain
Pretrained foundation models have exhibited extraordinary in-context learning performance, allowing zero-shot generalization to new tasks not encountered during pretraining. In the…
Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples
Weiqin Chen, Xinjie Zhang, Sandipan Mishra +1
Offline reinforcement learning (RL) learns effective policies from a static target dataset. The performance of state-of-the-art offline RL algorithms notwithstanding, it relies on…
Multi-agent assignment via state augmented reinforcement learning
Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana +2
We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techni…
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
Weiqin Chen, Mark S. Squillante, Chai Wah Wu +1
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as…
Adaptive Primal-Dual Method for Safe Reinforcement Learning
Weiqin Chen, James Onyejizu, Long Vu +5
Primal-dual methods have a natural application in Safe Reinforcement Learning (SRL), posed as a constrained policy optimization problem. In practice however, applying primal-dual m…
Probabilistic Constraint for Safety-Critical Reinforcement Learning
Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, w…
Policy Gradients for Probabilistic Constrained Reinforcement Learning
Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
This paper considers the problem of learning safe policies in the context of reinforcement learning (RL). In particular, we consider the notion of probabilistic safety. This is, we…
Constrained Learning with Non-Convex Losses
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana +1
Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to im…
Towards Safe Continuing Task Reinforcement Learning
Miguel Calvo-Fullana, Luiz F. O. Chamon, Santiago Paternain
Safety is a critical feature of controller design for physical systems. When designing control policies, several approaches to guarantee this aspect of autonomy have been proposed,…
Sufficiently Accurate Model Learning for Planning
Clark Zhang, Santiago Paternain, Alejandro Ribeiro
Data driven models of dynamical systems help planners and controllers to provide more precise and accurate motions. Most model learning algorithms will try to minimize a loss funct…
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon +1
A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. In this class of problems, we show a sim…
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Prediction credibility measures, in the form of confidence intervals or probability distributions, are fundamental in statistics and machine learning to characterize model robustne…
Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes
Santiago Paternain, Juan Andres Bazerque, Alejandro Ribeiro
Reinforcement learning considers the problem of finding policies that maximize an expected cumulative reward in a Markov decision process with unknown transition probabilities. In…
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain +2
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms g…
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…
The empirical duality gap of constrained statistical learning
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana +1
This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information proce…
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…
A Prediction-Correction Algorithm for Real-Time Model Predictive Control
Santiago Paternain, Manfred Morari, Alejandro Ribeiro
In this work we adapt a prediction-correction algorithm for continuous time-varying convex optimization problems to solve dynamic programs arising from Model Predictive Control. In…
Safe Policies for Reinforcement Learning via Primal-Dual Methods
Santiago Paternain, Miguel Calvo-Fullana, Luiz F. O. Chamon +1
In this paper, we study the learning of safe policies in the setting of reinforcement learning problems. This is, we aim to control a Markov Decision Process (MDP) of which we do n…
Constrained Reinforcement Learning Has Zero Duality Gap
Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana +1
Autonomous agents must often deal with conflicting requirements, such as completing tasks using the least amount of time/energy, learning multiple tasks, or dealing with multiple o…
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…
Navigation of a Quadratic Potential with Ellipsoidal Obstacles
Harshat Kumar, Santiago Paternain, Alejandro Ribeiro
Given a convex quadratic potential of which its minimum is the agent's goal and a Euclidean space populated with ellipsoidal obstacles, one can construct a Rimon-Koditschek (RK) ar…