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

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-05
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING MAGAZINE, v.37, no.3, pp.123 - 135

ISSN
1053-5888
DOI
10.1109/MSP.2020.2976000
URI
http://hdl.handle.net/10203/274453
Appears in Collection
EE-Journal Papers(저널논문)
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