Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport Article Swipe
Kazuki Shibata
,
T. Jimbo
,
Takamitsu Matsubara
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.15260
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.15260
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.
Related Topics
Concepts
Reinforcement learning
Computer science
Controller (irrigation)
Event (particle physics)
Control (management)
Exploit
Telecommunications network
Multi-agent system
Distributed computing
Artificial neural network
Artificial intelligence
Computer network
Physics
Biology
Computer security
Quantum mechanics
Agronomy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.15260
- https://arxiv.org/pdf/2103.15260
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3157820316
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3157820316Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.15260Digital Object Identifier
- Title
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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transportWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-03-29Full publication date if available
- Authors
-
Kazuki Shibata, T. Jimbo, Takamitsu MatsubaraList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.15260Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.15260Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.15260Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Controller (irrigation), Event (particle physics), Control (management), Exploit, Telecommunications network, Multi-agent system, Distributed computing, Artificial neural network, Artificial intelligence, Computer network, Physics, Biology, Computer security, Quantum mechanics, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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