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Optimization for Reinforcement Learning: From a single agent to cooperative agents
IEEE Signal Processing Magazine ( IF 14.9 ) Pub Date : 2020-05-01 , DOI: 10.1109/msp.2020.2976000
Donghwan Lee , Niao He , Parameswaran Kamalaruban , Volkan Cevher

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.

中文翻译:

强化学习的优化:从单一代理到合作代理

在深度神经网络的最新进展的推动下,强化学习 (RL) 一直备受关注,因为人工智能最近取得了许多突破,包括在游戏中击败人类(例如国际象棋、围棋、星际争霸)、自动驾驶汽车、智能家庭自动化和服务机器人等。尽管取得了这些非凡的成就,但许多基本任务仍然无法由单个 RL 代理完成。例子比比皆是,从多人游戏、多机器人、蜂窝天线倾斜控制、交通控制系统和智能电网到网络管理。
更新日期:2020-05-01
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