10 citations · 15 across the 4 of their papers we have counts for
14 papers
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…
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…
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…