Publication Details

SELECT * FROM publications WHERE Record_Number=2672
Reference TypeConference Proceedings
Author(s)Peters, J.;Schaal, S.
Year2007
TitleUsing reward-weighted regression for reinforcement learning of task space control
Journal/Conference/Book TitleProceedings of the 2007 IEEE International 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.
Place PublishedHonolulu, Hawaii, April 1-5, 2007
Short TitleUsing reward-weighted regression for reinforcement learning of task space control
URL(s) https://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Publications/ADPRL2007-Peters_[0].pdf

  

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