DocumentCode
2717600
Title
Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark
Author
Riedmiller, Martin ; Peters, Jan ; Schaal, Stefan
Author_Institution
Neuroinformatics Group, Osnabrueck Univ.
fYear
2007
fDate
1-5 April 2007
Firstpage
254
Lastpage
261
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
Keywords
finite difference methods; gradient methods; learning (artificial intelligence); search problems; cart pole regulator benchmark; finite difference gradients; model-free policy gradient methods; natural policy gradients; parameterized policy search algorithms; vanilla policy gradients; Dynamic programming; Finite difference methods; Gradient methods; Learning; Legged locomotion; Motor drives; Optimization methods; Regulators; Solids; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0706-0
Type
conf
DOI
10.1109/ADPRL.2007.368196
Filename
4220841
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