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Titel:

Neural Value Function Approximation in Continuous State Reinforcement Learning Problems

Dokumenttyp:
Konferenzbeitrag
Autor(en):
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-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
European Workshop on Reinforcement Learning 14 (2018)
Kongress / Zusatzinformationen:
Lille, France
Datum der Konferenz:
01.-03. Oct. 2018
Jahr:
2018
Jahr / Monat:
2018-10
Monat:
Oct
Reviewed:
ja
Sprache:
en
TUM Einrichtung:
Lehrstuhl für Datenverarbeitung
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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