Graph Kernels and Gaussian Processes for Relational Reinforcement Learning

Graph Kernels and Gaussian Processes for Relational Reinforcement Learning (2003)

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by Thomas Gärtner , Kurt Driessens , Jan Ramon

Venue:	Machine Learning
Citations:	34 - 7 self

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@INPROCEEDINGS{Gärtner03graphkernels,
    author = {Thomas Gärtner and Kurt Driessens and Jan Ramon},
    title = {Graph Kernels and Gaussian Processes for Relational Reinforcement Learning},
    booktitle = {Machine Learning},
    year = {2003},
    pages = {146--163},
    publisher = {Springer}
}

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Abstract

Relational reinforcement learning is a Q-learning technique for relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between state-action pairs and their so called Q(uality)-value has to be not only very reliable, but it also has to be able to handle the relational representation of state-action pairs. In this paper we investigate...

Citations

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