Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning ...

© 2018, Springer Science+Business Media B.V., part of Springer Nature.The main goal of this article is to present COACH (COrrective Advice Communicated by Humans), a new learning framework that allows non-expert humans to ...

El objetivo del presente artículo es pro­poner un modelo para el análisis empírico del aprendizaje generado en las políticas pú­blicas. El modelo fue aplicado en el Instituto Coahuilense de Acceso a la Información Pú­blica ...

By the early nineties a Chilean network on computers and education for public schools had emerged. There were both high expectancies that technology could revolutionize education as well as divergent voices that doubted ...

We investigate the accuracy of copper price forecasts produced by three decision learning methods. Prior evidence (Liu et al. Resources Policy, 2017) shows that a regression tree, a simple decision learning model, can be ...

© 2019 INFORM. We present a framework for a class of sequential decision-making problems in the context of general interdiction problems, in which a leader and a follower repeatedly interact. At each period, the leader ...

Prevention of obesity requires policies that work. In this Series paper, we propose a new way to understand how food policies could be made to work more effectively for obesity prevention. Our approach draws on evidence ...

There is a growing use of resilience ideas within the disaster risk management literature and policy domain. However, few empirical studies have focused on how resilience ideas are conceptualized by practitioners, as ...