Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning

Abstract

We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuous action space and organizes them in a hierarchical tree structure. In this tree, each subtree holds a subset of the action samples and thus holds knowledge about a subregion of… (More)
DOI: 10.1007/978-3-642-15822-3_24

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Cite this paper

@inproceedings{Vollmer2010ExploringCA, title={Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning}, author={Christian Vollmer and Erik Schaffernicht and Horst-Michael Gro\ss}, booktitle={ICANN}, year={2010} }