174 citations · 290 across the 18 of their papers we have counts for
6 papers · 1 filter
Assured RL: Reinforcement Learning with Almost Sure Constraints
Agustin Castellano, Juan Bazerque, Enrique Mallada
We consider the problem of finding optimal policies for a Markov Decision Process with almost sure constraints on state transitions and action triplets. We define value and action-…
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…
Quadratic approximate dynamic programming for scheduling water resources: a case study
Agustin Castellano, Camila Martínez, Pablo Monzón +3
We address the problem of scheduling water resources in a power system via approximate dynamic programming.To this goal, we model a finite horizon economic dispatch problemwith con…
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…
Learning to be safe, in finite time
Agustin Castellano, Juan Bazerque, Enrique Mallada
This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an…
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…