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  Model-Based Reinforcement Learning with Continuous States and Actions

Deisenroth, M., Rasmussen, C., & Peters, J. (2008). Model-Based Reinforcement Learning with Continuous States and Actions. In M. Verleysen (Ed.), Advances in computational intelligence and learning: 16th European Symposium on Artificial Neural Networks (pp. 19-24). Evere, Belgium: d-side.

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ESANN-2008-Deisenroth.pdf (Any fulltext), 300KB
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 Creators:
Deisenroth, MP, Author           
Rasmussen, CE1, 2, Author           
Peters, J1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment.

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 Dates: 2008-04
 Publication Status: Published in print
 Pages: -
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 Identifiers: URI: http://www.dice.ucl.ac.be/esann/index.php?pg=pgm
BibTex Citekey: 4977
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Title: 16th European Symposium on Artificial Neural Networks (ESANN 2008)
Place of Event: Bruges, Belgium
Start-/End Date: 2008-04-23 - 2008-04-25

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Title: Advances in computational intelligence and learning: 16th European Symposium on Artificial Neural Networks
Source Genre: Proceedings
 Creator(s):
Verleysen, M, Editor
Affiliations:
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Publ. Info: Evere, Belgium : d-side
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 19 - 24 Identifier: -