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Conference Paper

Hierarchical Relative Entropy Policy Search


Peters,  J
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Daniel, C., Neumann, G., & Peters, J. (2012). Hierarchical Relative Entropy Policy Search. In N. Lawrence (Ed.), Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS) (pp. 273-281). Cambridge, MA, USA: Microtome Publ.

Cite as:
{Many real hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend th Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach.}