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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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.2008.912000 ◽

2009 ◽

Author(s):

Bart Baddeley

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Time And Space ◽

Ill Conditioning

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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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Linear inverse reinforcement learning in continuous time and space

2018 Annual American Control Conference (ACC) ◽

10.23919/acc.2018.8431430 ◽

2018 ◽

Author(s):

Rushikesh Kamalapurkar

Keyword(s):

Reinforcement Learning ◽

Continuous Time ◽

Inverse Reinforcement Learning ◽

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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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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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Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach

SSRN Electronic Journal ◽

10.2139/ssrn.3905379 ◽

2021 ◽

Author(s):

Yanwei Jia ◽

Xunyu Zhou

Keyword(s):

Policy Evaluation ◽

Continuous Time ◽

Temporal Difference ◽

Temporal Difference Learning ◽

Time And Space ◽

Martingale Approach

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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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Constrained Search in Continuous Time and Space

International Series in Operations Research & Management Science - Optimal Search for Moving Targets ◽

10.1007/978-3-319-26899-6_6 ◽

2016 ◽

pp. 155-188

Author(s):

Lawrence D. Stone ◽

Johannes O. Royset ◽

Alan R. Washburn

Keyword(s):

Continuous Time ◽

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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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