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 ...
We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJ...
Dynamic reconfiguration allows modifying a system during its execution, and can be used to apply patches and updates, to implement adaptive systems, dynamic instrumentation, or to...
Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain)...
Action set selection in Markov Decision Processes (MDPs) is an area of research that has received little attention. On the other hand, the set of actions available to an MDP agent...