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research article

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Lee, Donghwan
•
He, Niao
•
Kamalaruban, Parameswaran  
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May 1, 2020
IEEE Signal Processing Magazine

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, smart-home automation, and service robots, among many others. Despite these remarkable achievements, many basic tasks can still elude a single RL agent. Examples abound, from multiplayer games, multirobots, cellular-antenna tilt control, traffic-control systems, and smart power grids to network management.

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Type
research article
DOI
10.1109/MSP.2020.2976000
Web of Science ID

WOS:000532218500015

Author(s)
Lee, Donghwan
He, Niao
Kamalaruban, Parameswaran  
Cevher, Volkan  orcid-logo
Date Issued

2020-05-01

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Published in
IEEE Signal Processing Magazine
Volume

37

Issue

3

Start page

123

End page

135

Subjects

Engineering, Electrical & Electronic

•

Engineering

•

consensus

•

coordination

•

learners

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIONS  
Available on Infoscience
May 28, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/168985
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