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Title:

Neural Value Function Approximation in Continuous State Reinforcement Learning Problems

Document type:
Konferenzbeitrag
Author(s):
Gottwald, Martin; Guo, Mingpan; Shen, Hao
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
Reviewed:
ja
Language:
en
TUM Institution:
Lehrstuhl für Datenverarbeitung
CC license:
by, http://creativecommons.org/licenses/by/4.0
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