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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
Meyer, Dominik; Knopp, Martin; Shen, Hao 
Title:
Bootstrapped Gradient Temporal-Difference Learning 
Abstract:
In this work we aim at providing a overview on gradient based temporal difference learning methods in reinforcement learning. We will look at three different cost functions, the mean squared Bellman error, the mean squared projected Bellman error and the norm of the expected update. Finally we will derive two new on-line gradient algorithms for TD learning, that base on the idea of bootstrapping. 
Keywords:
Reinforcement Learning (RL); Stochastic Gradient Descent; Bootstrapping; Gradient Temporal-Difference (GTD) 
Book / Congress title:
DGRTage 2013 
Year:
2013 
Quarter:
4. Quartal 
Year / month:
2014-10 
Month:
Oct 
Pages:
Reviewed:
ja 
Language:
en