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Solving Deep Memory POMDPs with Recurrent Policy Gradients

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Förster A, Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Wierstra, D., Förster A, Peters, J., & Schmidhuber, J. (2007). Solving Deep Memory POMDPs with Recurrent Policy Gradients. Artificial Neural Networks: ICANN 2007, 697-706.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CBF9-E
Zusammenfassung
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.