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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 Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

Control

Record Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

Learning Motor Primitives

Record Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

Robotics

Record Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

Human Motor Control

Record Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

Book Reviews

Record Number2654
Reference TypeConference Proceedings
Author(s)Riedmiller, M.;Peters, J.;Schaal, S.
Year2007
TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
Journal/Conference/Book TitleProceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning
Keywordsreinforcement learning, cart-pole, policy gradient methods
AbstractIn 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.
Notesclmc
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleEvaluation of policy gradient methods and variants on the cart-pole benchmark
URL(s) http://www-clmc.usc.edu/publications/P/riedmiller-ADPRL2007.pdf

The majority of the publications can also be obtained by Google Scholar where incomplete lists of citations are also given.


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