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Published in:
2007 | OriginalPaper | Chapter
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Authors : Daan Wierstra, Alexander Foerster, Jan Peters, Jürgen Schmidhuber
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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This paper presents Recurrent Policy Gradients, a model-free reinforcement learning (RL) method creating limited-memory sto-chastic 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.