Stefan Faußer

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Having a large game-tree complexity and being EXPTIME-complete, English Draughts, recently weakly solved [4] during almost two decades, is still hard to learn for intelligent computer agents. In this paper we present a Temporal-Difference method that is nonlinear neural approximated by a 4-layer multi-layer perceptron. We have built multiple English(More)
Ensemble models can achieve more accurate predictions than single learners. Selective ensembles further improve the predictions by selecting an informative subset of the full ensemble. We consider reinforcement learning ensembles, where the members are neural networks. In this context we study a new algorithm for ensemble subset selection in reinforcement(More)
The integration of function approximation methods into reinforcement learning models allows for learning state- and state-action values in large state spaces. Model-free methods, like temporal-difference or SARSA, yield good results for problems where the Markov property holds. However, methods based on a temporal-difference are known to be unstable(More)
Ensemble methods allow to combine multiple models to increase the predictive performances but mostly utilize labelled data. In this paper we propose several ensemble methods to learn a combined parameterized state-value function of multiple agents. For this purpose the Temporal-Difference (TD) and Residual-Gradient (RG) update methods as well as a policy(More)
Please cite this article in press as: Faußer, S., Sc (2013), Labelling real world data sets is a difficult problem. Often, the human expert is unsure about a class label of a specific sample point or, in case of very large data sets, it is impractical to label them manually. In semi-supervised clustering, the sample(More)