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An empirical study of reward structures for actor-critic reinforcement learning in air combat manoeuvring simulation

conference contribution
posted on 2019-01-01, 00:00 authored by B Kurniawan, P Vamplew, M Papasimeon, Richard DazeleyRichard Dazeley, C Foale
Reinforcement learning techniques for solving complex problems are resource-intensive and take a long time to converge, prompting a need for methods that encourage faster learning. In this paper we show our successful application of actor-critic reinforcement learning to the air combat simulation domain and how reward structures affect the learning speed to find effective air combat tactics.

History

Event

Artificial Intelligence. Conference (32nd : 2019 : Adelaide, S. Aust.)

Volume

11919

Series

Artificial Intelligence Conference

Pagination

54 - 65

Publisher

Springer

Location

Adelaide, S. Aust.

Place of publication

Cham, Switzerland

Start date

2019-12-02

End date

2019-12-05

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030352875

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Jixue Liu, James Bailey

Title of proceedings

AI 2019: Advances in artificial intelligence : Proceedings of the 32nd Australian Joint Conference on Artificial Intelligence 2019

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