Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning

JL Junell; T Mannucci; Y Zhou; EJ van Kampen

doi:10.2514/6.2016-1387

Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning

JL Junell, T Mannucci, Y Zhou, EJ van Kampen

Control & Simulation

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

11 Citations (Scopus)

216 Downloads (Pure)

Original language	English
Title of host publication	Proceedings of the AIAA guidance, navigation, and control conference
Editors	s.n.
Place of Publication	Reston
Publisher	American Institute of Aeronautics and Astronautics Inc. (AIAA)
Pages	1-15
Number of pages	15
ISBN (Print)	978-1-62410-389-6
DOIs	https://doi.org/10.2514/6.2016-1387
Publication status	Published - 2016
Event	AIAA Guidance, Navigation, and Control Conference, 2016 - San Diego, United States Duration: 4 Jan 2016 → 8 Jan 2016

Publication series

Name
Publisher	AIAA

Conference

Conference	AIAA Guidance, Navigation, and Control Conference, 2016
Country/Territory	United States
City	San Diego
Period	4/01/16 → 8/01/16

Bibliographical note

harvest
AIAA 2016-1387

Access to Document

10.2514/6.2016-1387

aiaa_stg_postprintversionAccepted author manuscript, 3.08 MB

Cite this

Junell, JL., Mannucci, T., Zhou, Y., & van Kampen, EJ. (2016). Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning. In s.n. (Ed.), Proceedings of the AIAA guidance, navigation, and control conference (pp. 1-15). Article AIAA 2016-1387 American Institute of Aeronautics and Astronautics Inc. (AIAA). https://doi.org/10.2514/6.2016-1387

@inproceedings{9d66a7da17c743cfb1c6312605581c80,

title = "Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning",

author = "JL Junell and T Mannucci and Y Zhou and {van Kampen}, EJ",

note = "harvest AIAA 2016-1387; AIAA Guidance, Navigation, and Control Conference, 2016 ; Conference date: 04-01-2016 Through 08-01-2016",

year = "2016",

doi = "10.2514/6.2016-1387",

language = "English",

isbn = "978-1-62410-389-6",

publisher = "American Institute of Aeronautics and Astronautics Inc. (AIAA)",

pages = "1--15",

editor = "s.n.",

booktitle = "Proceedings of the AIAA guidance, navigation, and control conference",

address = "United States",

}

Junell, JL, Mannucci, T, Zhou, Y & van Kampen, EJ 2016, Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning. in s.n. (ed.), Proceedings of the AIAA guidance, navigation, and control conference., AIAA 2016-1387, American Institute of Aeronautics and Astronautics Inc. (AIAA), Reston, pp. 1-15, AIAA Guidance, Navigation, and Control Conference, 2016, San Diego, United States, 4/01/16. https://doi.org/10.2514/6.2016-1387

Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning. / Junell, JL; Mannucci, T; Zhou, Y et al.
Proceedings of the AIAA guidance, navigation, and control conference. ed. / s.n. Reston: American Institute of Aeronautics and Astronautics Inc. (AIAA), 2016. p. 1-15 AIAA 2016-1387.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning

AU - Junell, JL

AU - Mannucci, T

AU - Zhou, Y

AU - van Kampen, EJ

N1 - harvest AIAA 2016-1387

PY - 2016

Y1 - 2016

UR - http://resolver.tudelft.nl/uuid:9d66a7da-17c7-43cf-b1c6-312605581c80

U2 - 10.2514/6.2016-1387

DO - 10.2514/6.2016-1387

M3 - Conference contribution

SN - 978-1-62410-389-6

SP - 1

EP - 15

BT - Proceedings of the AIAA guidance, navigation, and control conference

A2 - s.n., null

PB - American Institute of Aeronautics and Astronautics Inc. (AIAA)

CY - Reston

T2 - AIAA Guidance, Navigation, and Control Conference, 2016

Y2 - 4 January 2016 through 8 January 2016

ER -

Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Cite this