Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

Lukas Kesper; Sebastian Trimpe; Dominik Baumann

Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

Lukas Kesper, Sebastian Trimpe, Dominik Baumann

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

62 Lataukset (Pure)

Abstrakti

Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the Learning for Dynamics and Control Conference
Kustantaja	JMLR
Sivut	1072-1085
Sivumäärä	14
Vuosikerta	211
Tila	Julkaistu - 1 kesäk. 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	Learning for Dynamics and Control - University of Pennsylvania, Philadelphia, Yhdysvallat Kesto: 14 kesäk. 2023 → 16 kesäk. 2023 Konferenssinumero: 5 https://l4dc.seas.upenn.edu/

Julkaisusarja

Nimi	Proceedings of Machine Learning Research
ISSN (elektroninen)	2640-3498

Conference

Conference	Learning for Dynamics and Control
Lyhennettä	L4DC
Maa/Alue	Yhdysvallat
Kaupunki	Philadelphia
Ajanjakso	14/06/2023 → 16/06/2023
www-osoite	https://l4dc.seas.upenn.edu/

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@inproceedings{d7cea6607bd94e50a2a8eddf5b61f53d,

title = "Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control",

abstract = "Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.",

keywords = "Electrical Engineering and Systems Science - Systems and Control",

author = "Lukas Kesper and Sebastian Trimpe and Dominik Baumann",

year = "2023",

month = jun,

day = "1",

language = "English",

volume = "211",

series = "Proceedings of Machine Learning Research",

publisher = "JMLR",

pages = "1072--1085",

booktitle = "Proceedings of the Learning for Dynamics and Control Conference",

address = "United States",

note = "Learning for Dynamics and Control, L4DC ; Conference date: 14-06-2023 Through 16-06-2023",

url = "https://l4dc.seas.upenn.edu/",

}

Kesper, L, Trimpe, S & Baumann, D 2023, Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control. julkaisussa Proceedings of the Learning for Dynamics and Control Conference. Vuosikerta 211, Proceedings of Machine Learning Research, JMLR, Sivut 1072-1085, Learning for Dynamics and Control, Philadelphia, Pennsylvania, Yhdysvallat, 14/06/2023. <http://10.48550/arXiv.2305.08723>

Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control. / Kesper, Lukas; Trimpe, Sebastian; Baumann, Dominik.
Proceedings of the Learning for Dynamics and Control Conference. Vuosikerta 211 JMLR, 2023. s. 1072-1085 (Proceedings of Machine Learning Research).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

AU - Kesper, Lukas

AU - Trimpe, Sebastian

AU - Baumann, Dominik

N1 - Conference code: 5

PY - 2023/6/1

Y1 - 2023/6/1

N2 - Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.

AB - Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.

KW - Electrical Engineering and Systems Science - Systems and Control

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M3 - Conference article in proceedings

VL - 211

T3 - Proceedings of Machine Learning Research

SP - 1072

EP - 1085

BT - Proceedings of the Learning for Dynamics and Control Conference

PB - JMLR

T2 - Learning for Dynamics and Control

Y2 - 14 June 2023 through 16 June 2023

ER -

Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

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