When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …

Importance Sampling (IS) is a widely used building block for a large variety of off-policy
estimation and learning algorithms. However, empirical and theoretical studies have …

Behavior constrained policy optimization has been demonstrated to be a successful
paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a …

Abstract The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of
new policies from the data collected by another one. OPE is crucial when evaluating a new …

Monte Carlo methods have become increasingly relevant for control of non-differentiable
systems, approximate dynamics models, and learning from data. These methods scale to …

Off-policy methods are the basis of a large number of effective Policy Optimization (PO)
algorithms. In this setting, Importance Sampling (IS) is typically employed for off-policy …

This paper presents a comparative analysis of six sampling techniques to identify an efficient
and accurate sampling technique to be applied to probabilistic voltage stability assessment …

By predicting a personalized ranking on a set of items, item recommendation helps users
determine the information they need. While optimizing a ranking-focused loss is more in line …

Learning in a lifelong setting, where the dynamics continually evolve, is a hard challenge for
current reinforcement learning algorithms. Yet this would be a much needed feature for …

Distribution drift is an important issue for practical applications of machine learning (ML). In
particular, in streaming ML, the data distribution may change over time, yielding the problem …