Continuous‐Time Reinforcement Learning for Force Control

Mapping Intimacies ◽

10.1002/9781119782773.ch7 ◽

2021 ◽

pp. 119-138

Keyword(s):

Reinforcement Learning ◽

Force Control ◽

Continuous Time

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Reinforcement Learning and Adaptive Optimal Control for Continuous-Time Nonlinear Systems: A Value Iteration Approach

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2020.3045087 ◽

2021 ◽

pp. 1-10

Author(s):

Tao Bian ◽

Zhong-Ping Jiang

Keyword(s):

Optimal Control ◽

Reinforcement Learning ◽

Nonlinear Systems ◽

Continuous Time ◽

Value Iteration ◽

Adaptive Optimal Control ◽

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Policy iterations for reinforcement learning problems in continuous time and space — Fundamental theory and methods

10.1016/j.automatica.2020.109421 ◽

2021 ◽

Vol 126 ◽

pp. 109421

Author(s):

Jaeyoung Lee ◽

Richard S. Sutton

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Learning Problems ◽

Fundamental Theory ◽

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Continuous-time on-policy neural Reinforcement Learning of working memory tasks

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280636 ◽

2015 ◽

Author(s):

Davide Zambrano ◽

Pieter R. Roelfsema ◽

Sander M. Bohte

Keyword(s):

Working Memory ◽

Reinforcement Learning ◽

Continuous Time

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Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2016.2582849 ◽

2017 ◽

Vol 28 (3) ◽

pp. 704-713 ◽

Author(s):

Ruizhuo Song ◽

Frank L. Lewis ◽

Qinglai Wei

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Learning Method

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Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks

Artificial Neural Networks and Machine Learning – ICANN 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01421-6_25 ◽

2018 ◽

pp. 250-262

Author(s):

Marios Karamanis ◽

Davide Zambrano ◽

Sander Bohté

Keyword(s):

Working Memory ◽

Reinforcement Learning ◽

Continuous Time

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Continuous‐time receding‐horizon reinforcement learning and its application to path‐tracking control of autonomous ground vehicles

Optimal Control Applications and Methods ◽

10.1002/oca.2832 ◽

2021 ◽

Author(s):

Yang Lu ◽

Wenzhang Li ◽

Xinglong Zhang ◽

Xin Xu

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Tracking Control ◽

Path Tracking ◽

Ground Vehicles ◽

Receding Horizon ◽

Autonomous Ground Vehicles

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Adaptive Reinforcement Learning Motion/Force Control of Multiple Uncertain Manipulators

Intelligent Systems and Networks - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-2094-2_33 ◽

2021 ◽

pp. 265-271

Author(s):

Phuong Nam Dao ◽

Dinh Duong Pham ◽

Xuan Khai Nguyen ◽

Tat Chung Nguyen

Keyword(s):

Reinforcement Learning ◽

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Finite-horizon optimal control for continuous-time uncertain nonlinear systems using reinforcement learning

International Journal of Systems Science ◽

10.1080/00207721.2020.1797223 ◽

2020 ◽

Vol 51 (13) ◽

pp. 2429-2440

Author(s):

Jingang Zhao ◽

Minggang Gan

Keyword(s):

Optimal Control ◽

Reinforcement Learning ◽

Nonlinear Systems ◽

Continuous Time ◽

Finite Horizon ◽

Uncertain Nonlinear Systems

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Event-Triggered Reinforcement Learning-Based Adaptive Tracking Control for Completely Unknown Continuous-Time Nonlinear Systems

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2019.2903108 ◽

2020 ◽

Vol 50 (7) ◽

pp. 3231-3242 ◽

Author(s):

Xinxin Guo ◽

Weisheng Yan ◽

Rongxin Cui

Keyword(s):

Reinforcement Learning ◽

Nonlinear Systems ◽

Continuous Time ◽

Tracking Control ◽

Adaptive Tracking Control ◽

Adaptive Tracking ◽

Event Triggered

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Reinforcement Learning in Continuous Time and Space: Interference and Not Ill Conditioning Is the Main Problem When Using Distributed Function Approximators

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/tsmcb.2008.921000 ◽

2008 ◽

Vol 38 (4) ◽

pp. 950-956 ◽

Author(s):

B. Baddeley

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Time And Space ◽

Ill Conditioning

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