Learning models of relational MDPs using graph kernels

Authors:
Florian Halbritter;Peter Geibel
Affiliations:
Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany;Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

Relational reinforcement learning is the application of reinforcement learning to structured state descriptions. Model-based methods learn a policy based on a known model that comprises a description of the actions and their effects as well as the reward function. If the model is initially unknown, one might learn the model first and then apply the model-based method (indirect reinforcement learning). In this paper, we propose a method for model-learning that is based on a combination of several SVMs using graph kernels. Indeterministic processes can be dealt with by combining the kernel approach with a clustering technique. We demonstrate the validity of the approach by a range of experiments on various Blocksworld scenarios.