Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems.
Ramón RotaecheAlberto BallesterosJulián ProenzaPublished in: ETFA (2021)
Keyphrases
- 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