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Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems.
Ramón Rotaeche
Alberto Ballesteros
Julián Proenza
Published in:
ETFA (2021)
Keyphrases
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embedded systems
reinforcement learning
low cost
embedded devices
computing power
resource limited
embedded software
real time embedded
computing platform
distributed systems
hardware software
real time systems
safety critical
real time image processing
multi agent
processing power
case study
flash memory
software systems
cooperative
resource allocation
cyber physical systems
protocol stack
embedded real time systems
field programmable gate array
wireless networks
consumer electronics
artificial intelligence