We consider online learning in repeated decision problems, within the framework of a repeated game against an arbitrary opponent. For repeated matrix games, well known results esta...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Enabling a sophisticated Demand Response (DR) framework, whereby individual consumers adapt their electricity consumption in response to price variations, is a major objective of ...
Support for distributed application management in large-scale networked environments remains in its early stages. Although a number of solutions exist for subtasks of application ...
Jeannie R. Albrecht, Ryan Braud, Darren Dao, Nikol...
Reward shaping is a well-known technique applied to help reinforcement-learning agents converge more quickly to nearoptimal behavior. In this paper, we introduce social reward sha...
Monica Babes, Enrique Munoz de Cote, Michael L. Li...