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

Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number9084325
Pages (from-to)123-135
Number of pages13
JournalIEEE Signal Processing Magazine
Issue number3
StatePublished - May 2020

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'Optimization for Reinforcement Learning: From a single agent to cooperative agents'. Together they form a unique fingerprint.

Cite this