22 citations · 76 across the 21 of their papers we have counts for
25 papers
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requir…
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual varia…
Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach
Francesc Wilhelmi, Boris Bellalta, Miguel Casasnovas +2
Spatial Reuse (SR) is a cost-effective technique for improving spectral efficiency in dense IEEE 802.11 deployments by enabling simultaneous transmissions. However, the decentraliz…
Cross-Learning from Scarce Data via Multi-Task Constrained Optimization
Leopoldo Agorio, Juan Cerviño, Miguel Calvo-Fullana +2
A learning task, understood as the problem of fitting a parametric model from supervised data, fundamentally requires the dataset to be large enough to be representative of the und…
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…
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 Networked Multi-Agent System for Mobile Wireless Infrastructure on Demand
Miguel Calvo-Fullana, Mikhail Gerasimenko, Daniel Mox +5
Despite the prevalence of wireless connectivity in urban areas around the globe, there remain numerous and diverse situations where connectivity is insufficient or unavailable. To…
Mission-Aware Value of Information Censoring for Distributed Filtering
Miguel Calvo-Fullana, Jonathan P. How
In this paper, we study the problem of distributed estimation with an emphasis on communication-efficiency. The proposed algorithm is based on a windowed maximum a posteriori (MAP)…
Multi-task Bias-Variance Trade-off Through Functional Constraints
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is t…
Distributed Filtering with Value of Information Censoring
Miguel Calvo-Fullana, Jonathan P. How
This work presents a distributed estimation algorithm that efficiently uses the available communication resources. The approach is based on Bayesian filtering that is distributed a…
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation
Yulun Tian, Amrit Singh Bedi, Alec Koppel +3
We present the first distributed optimization algorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localizati…
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,…
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…
ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators
Miguel Calvo-Fullana, Daniel Mox, Alexander Pyattaev +3
Multi-agent systems play an important role in modern robotics. Due to the nature of these systems, coordination among agents via communication is frequently necessary. Indeed, Perc…
Multi-task Supervised Learning via Cross-learning
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks. In our nov…
Multi-task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-learning
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
Reinforcement learning (RL) is a framework to optimize a control policy using rewards that are revealed by the system as a response to a control action. In its standard form, RL in…
Mobile Wireless Network Infrastructure on Demand
Daniel Mox, Miguel Calvo-Fullana, Mikhail Gerasimenko +3
In this work, we introduce Mobile Wireless In-frastructure on Demand: a framework for providing wireless connectivity to multi-robot teams via autonomously reconfiguring ad-hoc net…
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…
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…
Random Access Communication for Wireless Control Systems with Energy Harvesting Sensors
Miguel Calvo-Fullana, Carles Antón-Haro, Javier Matamoros +1
In this paper, we study wireless networked control systems in which the sensing devices are powered by energy harvesting. We consider a scenario with multiple plants, where the sen…
Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks
Miguel Calvo-Fullana, Carles Antón-Haro, Javier Matamoros +1
In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dua…
Reconstruction of Correlated Sources with Energy Harvesting Constraints in Delay-constrained and Delay-tolerant Communication Scenarios
Miguel Calvo-Fullana, Javier Matamoros, Carles Antón-Haro
In this paper, we investigate the reconstruction of time-correlated sources in a point-to-point communications scenario comprising an energy-harvesting sensor and a Fusion Center (…