Main » Publications by Topic
This list is automatically created, please see publications by year in order to have a more chronological overview on my publications. Note that the list on this page is automatically generated and as such always overlapping due to overlapping keywords.
Reinforcement Learning
| 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 |
Control
| 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 |
Learning Motor Primitives
| 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 |
Robotics
| 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 |
Human Motor Control
| 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 |
Book Reviews
| 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 |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.