Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector mach...
Gwen Littlewort, Marian Stewart Bartlett, Ian R. F...
Search personalization has been pursued in many ways, in order to provide better result rankings and better overall search experience to individual users [5]. However, blindly app...
Julia Luxenburger, Shady Elbassuoni, Gerhard Weiku...
Abstract. Molecular communication is a novel paradigm that uses molecules as an information carrier to enable nanomachines to communicate with each other. Controlled molecule deliv...
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference meth...
Doina Precup, Richard S. Sutton, Satinder P. Singh