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2003.02157
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Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
IEEE Transactions on Network and Service Management (TNSM), 2020
21 February 2020
M. S. Munir
S. F. Abedin
N. H. Tran
Zhu Han
Eui-nam Huh
Choong Seon Hong
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Papers citing
"Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach"
6 / 6 papers shown
Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review
Hafez Ghaemi
Shirin Jamshidi
Mohammad Mashreghi
M. N. Ahmadabadi
Hamed Kebriaei
313
2
0
10 Jun 2024
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
M. S. Munir
Sravanthi Proddatoori
Manjushree Muralidhara
Walid Saad
Zhu Han
Sachin Shetty
302
14
0
11 Mar 2024
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
Hafez Ghaemi
Hamed Kebriaei
Alireza Ramezani Moghaddam
Majid Nili Ahamadabadi
297
3
0
08 Feb 2024
Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges
N. Shalavi
Giovanni Perin
Andrea Zanella
M. Rossi
289
8
0
28 Sep 2022
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
Marie Siew
Shikhar Sharma
Zekai Li
Kun Guo
Chao Xu
Tania Lorido-Botran
Tony Q.S. Quek
Carlee Joe-Wong
379
1
0
28 Sep 2022
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
M. S. Munir
Ki Tae Kim
K. Thar
Dusit Niyato
Choong Seon Hong
186
9
0
02 Aug 2021
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