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Co-evolving recurrent neurons learn deep memory POMDPs
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GECCO
2005
Springer
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Optimization
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Co-evolving recurrent neurons learn deep memory POMDPs
15 years 8 months ago
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Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
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ICANN
2007
Springer
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Neural Networks
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ICANN 2007
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Solving Deep Memory POMDPs with Recurrent Policy Gradients
15 years 9 months ago
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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...
Daan Wierstra, Alexander Förster, Jan Peters,...
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