Results 231 to 240 of about 640,433 (264)

Reinforcement learning control

Current Opinion in Neurobiology, 1994
Reinforcement learning refers to improving performance through trial-and-error. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable 'teacher' able to tell them how to respond to a set of training stimuli ...
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Distributed Reinforcement Learning

Robotics and Autonomous Systems, 1995
In multi-agent systems two forms of learning can be distinguished: centralized learning, that is, learning done by a single agent independent of the other agents; and distributed learning, that is, learning that becomes possible only because several agents are present. Whereas centralized learning has been intensively studied in the field of artificial
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Reinforcement Learning and Deep Reinforcement Learning

2019
In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
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Reinforcement Learning Agents

Artificial Intelligence Review, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Reinforcement learning

2023
Jinna Li, Frank L. Lewis, Jialu Fan
  +4 more sources

Teaching Reinforcement Learning Agents via Reinforcement Learning

2023 57th Annual Conference on Information Sciences and Systems (CISS), 2023
Kun Yang, Chengshuai Shi, Cong Shen
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Reinforcement Learning

2022
Xiangming Zeng, Liangqu Long
  +5 more sources

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

IEEE Communications Surveys and Tutorials, 2021
Amal Feriani, Ekram Hossain
exaly  

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2022
Kun Zhu, Nguyen Cong Luong, Dusit Tao Niyato   +2 more
exaly  

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

IEEE Communications Surveys and Tutorials, 2021
Yang Xiao, Jun Liu, Nirwan Ansari
exaly