60 citations · 153 across the 26 of their papers we have counts for
5 papers · 1 filter
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 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…
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces
Santiago Paternain, Juan Andrés Bazerque, Austin Small +1
Reinforcement learning consists of finding policies that maximize an expected cumulative long-term reward in a Markov decision process with unknown transition probabilities and ins…