Push Recovery Strategies through Deep Reinforcement Learning

2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE) ◽

10.1109/lars/sbr/wre51543.2020.9306967 ◽

2020 ◽

Author(s):

Dicksiano Carvalho Melo ◽

Marcos R. O. A. Maximo ◽

Adilson Marques da Cunha

Keyword(s):

Reinforcement Learning ◽

Push Recovery ◽

Recovery Strategies

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Push Recovery Control for Humanoid Robot Using Reinforcement Learning

2019 Third IEEE International Conference on Robotic Computing (IRC) ◽

10.1109/irc.2019.00102 ◽

2019 ◽

Author(s):

Harin Kim ◽

Donghyeon Seo ◽

Donghan Kim

Keyword(s):

Reinforcement Learning ◽

Humanoid Robot ◽

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Push-recovery strategies implemented on a compliant humanoid robot

Neuroscience Research ◽

10.1016/j.neures.2009.09.980 ◽

2009 ◽

Vol 65 ◽

pp. S183

Author(s):

Sang-Ho Hyon ◽

Rieko Osu ◽

Yohei Otaka ◽

Jun Morimoto

Keyword(s):

Humanoid Robot ◽

Push Recovery ◽

Recovery Strategies

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Biologically motivated push recovery strategies for a 3D bipedal robot walking in complex environments

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO) ◽

10.1109/robio.2013.6739637 ◽

2013 ◽

Author(s):

Jie Zhao ◽

Steffen Schutz ◽

Karsten Berns

Keyword(s):

Complex Environments ◽

Bipedal Robot ◽

Push Recovery ◽

Recovery Strategies

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A strategy for push recovery in quadruped robot based on reinforcement learning

2015 34th Chinese Control Conference (CCC) ◽

10.1109/chicc.2015.7260125 ◽

2015 ◽

Author(s):

Yang-zhen Chen ◽

Wen-Qi Hou ◽

Jian Wang ◽

Jian-Wen Wang ◽

Hong-xu Ma

Keyword(s):

Reinforcement Learning ◽

Quadruped Robot ◽

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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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