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  Solving Deep Memory POMDPs with Recurrent Policy Gradients

Wierstra, D., Förster, A., Peters, J., & Schmidhuber, J. (2007). Solving Deep Memory POMDPs with Recurrent Policy Gradients. In J. Marques de Sá, L. Alexandre, W. Duch, & D. Mandic (Eds.), Artificial Neural Networks – ICANN 2007: 7th International Conference, Porto, Portugal, September 9-13, 2007 (pp. 697-706). Berlin, Germany: Springer.

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 Creators:
Wierstra, D, Author
Förster, A, Author
Peters, J1, Author              
Schmidhuber, J, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov decision problems (POMDPs) that require long-term memories of past observations. The approach involves approximating a policy gradient for a Recurrent Neural Network (RNN) by backpropagating return-weighted characteristic eligibilities through time. Using a “Long Short-Term Memory” architecture, we are able to outperform other RL methods on two important benchmark tasks. Furthermore, we show promising results on a complex car driving simulation task.

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 Dates: 2007-09
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-74690-4_71
BibTex Citekey: 4719
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Title: International Conference on Artificial Neural Networks (ICANN 2007)
Place of Event: Porto, Portugal
Start-/End Date: 2007-09-09 - 2007-09-13

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Title: Artificial Neural Networks – ICANN 2007: 7th International Conference, Porto, Portugal, September 9-13, 2007
Source Genre: Proceedings
 Creator(s):
Marques de Sá, J, Editor
Alexandre, LA, Editor
Duch, W, Editor
Mandic, D, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 697 - 706 Identifier: ISBN: 978-3-540-74689-8

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 4668 Sequence Number: - Start / End Page: - Identifier: -