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 | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
Control
| Record Number | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
Learning Motor Primitives
| Record Number | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
Robotics
| Record Number | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
Human Motor Control
| Record Number | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
Book Reviews
| Record Number | 2672 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Using reward-weighted regression for reinforcement learning of task space control |
| Journal/Conference/Book Title | Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning |
| Keywords | reinforcement learning, cart-pole, policy gradient methods |
| Abstract | In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, `vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease. |
| Notes | clmc |
| Place Published | Honolulu, Hawaii, April 1-5, 2007 |
| Short Title | Using reward-weighted regression for reinforcement learning of task space control |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-ADPRL2007.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.