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Rollout sampling approximate policy iteration

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Robust Bayesian reinforcement learning through tight lower bounds

Christos Dimitrakakis

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this ...

2011

Preference elicitation and inverse reinforcement learning

Christos Dimitrakakis

We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a pos ...

2011

Automatic Configuration of an Autonomic Controller: An Experimental Study with Zero-Configuration Policies

Thomas Heinis

Autonomic control managers can remove the need for manual system configuration in order to achieve good performance and efficient resource utilization. However, simple controllers based on reconfiguration actions tied to thresholds, or 'if-then' rules, the ...

2008

Ensembles for sequence learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

EPFL2007

Ensembles for Sequence Learning

Christos Dimitrakakis

École Polytechnique Fédérale de Lausanne2006

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is ...

2005

Learning and Measuring Specialization in Collaborative Swarm Systems

Alcherio Martinoli

This paper addresses qualitative and quantitative diversity and specialization issues in the framework of selforganizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous sw ...

2004