Deep reinforcement learning with shallow controllers: An experimental application to PID tuning

Control Engineering Practice ◽

10.1016/j.conengprac.2021.105046 ◽

2022 ◽

Vol 121 ◽

pp. 105046

Author(s):

Nathan P. Lawrence ◽

Michael G. Forbes ◽

Philip D. Loewen ◽

Daniel G. McClement ◽

Johan U. Backström ◽

...

Keyword(s):

Reinforcement Learning ◽

Experimental Application

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Reinforcement learning approach for congestion management and cascading failure prevention with experimental application

Electric Power Systems Research ◽

10.1016/j.epsr.2016.06.041 ◽

2016 ◽

Vol 141 ◽

pp. 179-190 ◽

Author(s):

Sina Zarrabian ◽

Rabie Belkacemi ◽

Adeniyi A. Babalola

Keyword(s):

Reinforcement Learning ◽

Congestion Management ◽

Cascading Failure ◽

Learning Approach ◽

Failure Prevention ◽

Experimental Application

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The application of continuous action reinforcement learning automata to adaptive PID tuning

10.1049/ic:20000343 ◽

2000 ◽

Author(s):

M.N. Howell

Keyword(s):

Reinforcement Learning ◽

Learning Automata ◽

Continuous Action ◽

Reinforcement Learning Automata

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On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata

Control Engineering Practice ◽

10.1016/s0967-0661(99)00141-0 ◽

2000 ◽

Vol 8 (2) ◽

pp. 147-154 ◽

Author(s):

M.N Howell ◽

M.C Best

Keyword(s):

Reinforcement Learning ◽

Learning Automata ◽

Speed Control ◽

Continuous Action ◽

Idle Speed Control ◽

On Line ◽

Reinforcement Learning Automata

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Supplemental Material for Reconciling Reinforcement Learning Models With Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

Psychological Review ◽

10.1037/0033-295x.114.3.784.supp ◽

2007 ◽

Keyword(s):

Reinforcement Learning ◽

Problem Gambling ◽

Learning Models ◽

Behavioral Extinction ◽

Reinforcement Learning Models

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Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽

10.1037/dec0000040 ◽

2016 ◽

Vol 3 (2) ◽

pp. 115-131 ◽

Author(s):

Helen Steingroever ◽

Ruud Wetzels ◽

Eric-Jan Wagenmakers

Keyword(s):

Reinforcement Learning ◽

Iowa Gambling Task ◽

Bayes Factors ◽

Gambling Task ◽

Learning Models ◽

Reinforcement Learning Models

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Analogical Reinforcement Learning With Two-Stage Memory Retrieval

PsycEXTRA Dataset ◽

10.1037/e528942014-705 ◽

2014 ◽

Author(s):

James Foster ◽

Matt Jones

Keyword(s):

Reinforcement Learning ◽

Memory Retrieval ◽

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Effects of Working Memory Capacity on the Speed and Accuracy of Learning in Reinforcement Learning Models

PsycEXTRA Dataset ◽

10.1037/e528942014-552 ◽

2014 ◽

Author(s):

Adnane Ez-Zizi ◽

Simon Farrell ◽

David Leslie

Keyword(s):

Working Memory ◽

Reinforcement Learning ◽

Working Memory Capacity ◽

Memory Capacity ◽

Learning Models ◽

Reinforcement Learning Models ◽

Speed And Accuracy

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Supplemental Material for Reinforcement Learning Models of Risky Choice and the Promotion of Risk-Taking by Losses Disguised as Wins in Rats

Journal of Experimental Psychology Animal Learning and Cognition ◽

10.1037/xan0000141.supp ◽

2017 ◽

Keyword(s):

Reinforcement Learning ◽

Risk Taking ◽

Risky Choice ◽

Learning Models ◽

Losses Disguised As Wins ◽

Reinforcement Learning Models

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Reinforcement learning of irrelevant stimulus-response associations modulates cognitive control.

Journal of Experimental Psychology Learning Memory and Cognition ◽

10.1037/xlm0000850 ◽

2020 ◽

Author(s):

Jinglu Chen ◽

Ling Tan ◽

Lu Liu ◽

Ling Wang

Keyword(s):

Reinforcement Learning ◽

Cognitive Control ◽

Irrelevant Stimulus ◽

Stimulus Response

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A Collaborative Scheduling Lane Changing Model for Intelligent Connected Vehicles Based on Deep Reinforcement Learning

10.1061/9780784483053.178 ◽

2020 ◽

Author(s):

Zheyu Cui ◽

Jianming Hu

Keyword(s):

Reinforcement Learning ◽

Connected Vehicles ◽

Lane Changing ◽

Collaborative Scheduling

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