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Document type:
Konferenzbeitrag 
Author(s):
Gottwald, Martin; Guo, Mingpan; Shen, Hao 
Title:
Neural Value Function Approximation in Continuous State Reinforcement Learning Problems 
Abstract:
Recent development of Deep Reinforcement Learning (DRL) has demonstrated superior performance of neural networks in solving challenging problems with large or continuous state spaces. In this work, we focus on the problem of minimising the expected one step Temporal Difference (TD) error with neural function approximator for a continuous state space, from a smooth optimisation perspective. An approximate Newton’s algorithm is proposed. Effectiveness of the algorithm is demonstrated on both finit...    »
 
Dewey Decimal Classification:
620 Ingenieurwissenschaften 
Book / Congress title:
European Workshop on Reinforcement Learning 14 (2018) 
Congress (additional information):
Lille, France 
Date of congress:
01.-03. Oct. 2018 
Year:
2018 
Year / month:
2018-10 
Month:
Oct 
Language:
en 
TUM Institution:
Lehrstuhl für Datenverarbeitung 
CC license:
by, http://creativecommons.org/licenses/by/4.0