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| Record Number | 10132 |
| Reference Type | Conference Proceedings |
| Author(s) | Wierstra, D.; Foerster, A.; Peters, J.; Schmidhuber, J. |
| Year | 2007 |
| Title | Solving Deep Memory POMDPs with Recurrent Policy Gradients |
| Journal/Conference/Book Title | Proceedings of the International Conference on Artificial Neural Networks (ICANN) |
| Keywords | policy gradients, reinforcement learning |
| Abstract | This paper presents Recurrent Policy Gradients, a model- free 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. |
| Notes | jan |
| Link to PDF | http://www-clmc.usc.edu/publications//D/Wierstra_ICANN_2007.pdf |