---
_id: '30793'
author:
- first_name: Adrian
full_name: Redder, Adrian
id: '52265'
last_name: Redder
orcid: https://orcid.org/0000-0001-7391-4688
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Redder A, Ramaswamy A, Karl H. Multi-agent Policy Gradient Algorithms for
Cyber-physical Systems with Lossy Communication. In: Proceedings of the 14th
International Conference on Agents and Artificial Intelligence. SCITEPRESS
- Science and Technology Publications; 2022. doi:10.5220/0010845400003116'
apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Multi-agent Policy Gradient
Algorithms for Cyber-physical Systems with Lossy Communication. Proceedings
of the 14th International Conference on Agents and Artificial Intelligence.
https://doi.org/10.5220/0010845400003116
bibtex: '@inproceedings{Redder_Ramaswamy_Karl_2022, title={Multi-agent Policy Gradient
Algorithms for Cyber-physical Systems with Lossy Communication}, DOI={10.5220/0010845400003116},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial
Intelligence}, publisher={SCITEPRESS - Science and Technology Publications}, author={Redder,
Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }'
chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Multi-Agent Policy
Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” In Proceedings
of the 14th International Conference on Agents and Artificial Intelligence.
SCITEPRESS - Science and Technology Publications, 2022. https://doi.org/10.5220/0010845400003116.
ieee: 'A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms
for Cyber-physical Systems with Lossy Communication,” 2022, doi: 10.5220/0010845400003116.'
mla: Redder, Adrian, et al. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical
Systems with Lossy Communication.” Proceedings of the 14th International Conference
on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology
Publications, 2022, doi:10.5220/0010845400003116.
short: 'A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 14th International
Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology
Publications, 2022.'
date_created: 2022-04-06T07:18:36Z
date_updated: 2022-11-18T09:32:14Z
ddc:
- '006'
department:
- _id: '75'
doi: 10.5220/0010845400003116
file:
- access_level: closed
content_type: application/pdf
creator: aredder
date_created: 2022-08-31T07:10:13Z
date_updated: 2022-08-31T07:10:13Z
file_id: '33237'
file_name: ICCART2022.pdf
file_size: 298926
relation: main_file
success: 1
file_date_updated: 2022-08-31T07:10:13Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
- _id: '24'
name: 'NICCI-CN: Netzgewahre Regelung & regelungsgewahre Netze'
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
publication: Proceedings of the 14th International Conference on Agents and Artificial
Intelligence
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
status: public
title: Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy
Communication
type: conference
user_id: '477'
year: '2022'
...
---
_id: '15741'
abstract:
- lang: eng
text: "\r\nIn many cyber–physical systems, we encounter the problem of remote state
estimation of geo- graphically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors has
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to decide
which sensors have network access and which ones do not. To address this scheduling
problem, we formulate an associated Markov decision process (MDP). This MDP is
then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm
that is at once scalable and model-free. We compare our scheduling algorithm to
popular scheduling algorithms such as round-robin and reduced-waiting-time, among
others. Our algorithm is shown to significantly outperform these algorithms for
many example scenario"
article_number: '108759'
author:
- first_name: Alex S.
full_name: Leong, Alex S.
last_name: Leong
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Daniel E.
full_name: Quevedo, Daniel E.
last_name: Quevedo
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Ling
full_name: Shi, Ling
last_name: Shi
citation:
ama: Leong AS, Ramaswamy A, Quevedo DE, Karl H, Shi L. Deep reinforcement learning
for wireless sensor scheduling in cyber–physical systems. Automatica. 2019.
doi:10.1016/j.automatica.2019.108759
apa: Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., & Shi, L. (2019).
Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems.
Automatica. https://doi.org/10.1016/j.automatica.2019.108759
bibtex: '@article{Leong_Ramaswamy_Quevedo_Karl_Shi_2019, title={Deep reinforcement
learning for wireless sensor scheduling in cyber–physical systems}, DOI={10.1016/j.automatica.2019.108759},
number={108759}, journal={Automatica}, author={Leong, Alex S. and Ramaswamy, Arunselvan
and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}, year={2019} }'
chicago: Leong, Alex S., Arunselvan Ramaswamy, Daniel E. Quevedo, Holger Karl, and
Ling Shi. “Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber–Physical
Systems.” Automatica, 2019. https://doi.org/10.1016/j.automatica.2019.108759.
ieee: A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement
learning for wireless sensor scheduling in cyber–physical systems,” Automatica,
2019.
mla: Leong, Alex S., et al. “Deep Reinforcement Learning for Wireless Sensor Scheduling
in Cyber–Physical Systems.” Automatica, 108759, 2019, doi:10.1016/j.automatica.2019.108759.
short: A.S. Leong, A. Ramaswamy, D.E. Quevedo, H. Karl, L. Shi, Automatica (2019).
date_created: 2020-01-31T15:55:27Z
date_updated: 2022-01-06T06:52:32Z
ddc:
- '000'
department:
- _id: '7'
- _id: '34'
- _id: '3'
- _id: '75'
- _id: '57'
doi: 10.1016/j.automatica.2019.108759
file:
- access_level: closed
content_type: application/pdf
creator: hkarl
date_created: 2020-01-31T15:57:50Z
date_updated: 2020-01-31T15:57:50Z
file_id: '15743'
file_name: leoram20a.pdf
file_size: '675382'
relation: main_file
success: 1
file_date_updated: 2020-01-31T15:57:50Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '24'
name: Netzgewahre Regelung & regelungsgewahre Netze
publication: Automatica
publication_identifier:
issn:
- 0005-1098
publication_status: published
quality_controlled: '1'
status: public
title: Deep reinforcement learning for wireless sensor scheduling in cyber–physical
systems
type: journal_article
user_id: '126'
year: '2019'
...
---
_id: '2476'
author:
- first_name: Binyam
full_name: Shiferaw Heyi, Binyam
last_name: Shiferaw Heyi
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Shiferaw Heyi B, Karl H. Modelling Time-Limited Capacity of a Wireless Channel
as aMarkov Reward Process. In: Proc. of IEEE Wireless Communications and Networking
Conference (WCNC); 2018.'
apa: Shiferaw Heyi, B., & Karl, H. (2018). Modelling Time-Limited Capacity of
a Wireless Channel as aMarkov Reward Process. Proc. of IEEE Wireless Communications
and Networking Conference (WCNC).
bibtex: '@inproceedings{Shiferaw Heyi_Karl_2018, title={Modelling Time-Limited Capacity
of a Wireless Channel as aMarkov Reward Process}, publisher={Proc. of IEEE Wireless
Communications and Networking Conference (WCNC)}, author={Shiferaw Heyi, Binyam
and Karl, Holger}, year={2018} }'
chicago: Shiferaw Heyi, Binyam, and Holger Karl. “Modelling Time-Limited Capacity
of a Wireless Channel as AMarkov Reward Process.” Proc. of IEEE Wireless Communications
and Networking Conference (WCNC), 2018.
ieee: B. Shiferaw Heyi and H. Karl, “Modelling Time-Limited Capacity of a Wireless
Channel as aMarkov Reward Process,” 2018.
mla: Shiferaw Heyi, Binyam, and Holger Karl. Modelling Time-Limited Capacity
of a Wireless Channel as AMarkov Reward Process. Proc. of IEEE Wireless Communications
and Networking Conference (WCNC), 2018.
short: 'B. Shiferaw Heyi, H. Karl, in: Proc. of IEEE Wireless Communications and
Networking Conference (WCNC), 2018.'
date_created: 2018-04-24T08:09:00Z
date_updated: 2022-01-06T06:56:34Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: closed
content_type: application/pdf
creator: tabu
date_created: 2018-04-24T08:08:50Z
date_updated: 2018-04-24T08:08:50Z
file_id: '2477'
file_name: p2861-heyi.pdf
file_size: 428839
relation: main_file
success: 1
file_date_updated: 2018-04-24T08:08:50Z
has_accepted_license: '1'
project:
- _id: '24'
name: Netzgewahre Regelung & regelungsgewahre Netze
publisher: Proc. of IEEE Wireless Communications and Networking Conference (WCNC)
status: public
title: Modelling Time-Limited Capacity of a Wireless Channel as aMarkov Reward Process
type: conference
user_id: '15572'
year: '2018'
...
---
_id: '3217'
author:
- first_name: Burak
full_name: Demirel, Burak
last_name: Demirel
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
last_name: Ramaswamy
- first_name: Daniel
full_name: Quevedo, Daniel
last_name: Quevedo
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Demirel B, Ramaswamy A, Quevedo D, Karl H. DeepCAS: A Deep Reinforcement Learning
Algorithm for Control-Aware Scheduling. In: ; 2018. doi:10.1109/LCSYS.2018.2847721'
apa: 'Demirel, B., Ramaswamy, A., Quevedo, D., & Karl, H. (2018). DeepCAS: A
Deep Reinforcement Learning Algorithm for Control-Aware Scheduling. https://doi.org/10.1109/LCSYS.2018.2847721'
bibtex: '@inproceedings{Demirel_Ramaswamy_Quevedo_Karl_2018, title={DeepCAS: A Deep
Reinforcement Learning Algorithm for Control-Aware Scheduling}, DOI={10.1109/LCSYS.2018.2847721},
author={Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel and Karl,
Holger}, year={2018} }'
chicago: 'Demirel, Burak, Arunselvan Ramaswamy, Daniel Quevedo, and Holger Karl.
“DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling,”
2018. https://doi.org/10.1109/LCSYS.2018.2847721.'
ieee: 'B. Demirel, A. Ramaswamy, D. Quevedo, and H. Karl, “DeepCAS: A Deep Reinforcement
Learning Algorithm for Control-Aware Scheduling,” 2018.'
mla: 'Demirel, Burak, et al. DeepCAS: A Deep Reinforcement Learning Algorithm
for Control-Aware Scheduling. 2018, doi:10.1109/LCSYS.2018.2847721.'
short: 'B. Demirel, A. Ramaswamy, D. Quevedo, H. Karl, in: 2018.'
date_created: 2018-06-13T10:26:23Z
date_updated: 2022-01-06T06:59:05Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/LCSYS.2018.2847721
file:
- access_level: closed
content_type: application/pdf
creator: tabu
date_created: 2018-06-13T10:47:57Z
date_updated: 2018-06-13T10:47:57Z
file_id: '3218'
file_name: 1803.02998.pdf
file_size: 354166
relation: main_file
success: 1
file_date_updated: 2018-06-13T10:47:57Z
has_accepted_license: '1'
main_file_link:
- url: https://arxiv.org/pdf/1803.02998.pdf
project:
- _id: '24'
name: Netzgewahre Regelung & regelungsgewahre Netze
status: public
title: 'DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling'
type: conference
user_id: '126'
year: '2018'
...