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 | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
Control
| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
Learning Motor Primitives
| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
Robotics
| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
Human Motor Control
| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
Book Reviews
| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
| Abstract | Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots. |
| Notes | clmc |
| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space control |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf |
The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.