Real-time reinforcement learning by sequential actor-critics and experience replay

DOI10.1016/j.neunet.2009.05.011zbMath1396.68107OpenAlexW2089434629WikidataQ51616641 ScholiaQ51616641MaRDI QIDQ1784532

Paweł Wawrzyński

Publication date: 27 September 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.05.011

zbMATH Keywords

reinforcement learningmachine learningdirect adaptive controlexperience replay

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Adaptive control/observation systems (93C40)

Related Items (5)

Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning ⋮ Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration ⋮ Autonomous reinforcement learning with experience replay ⋮ Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems ⋮ Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

Cites Work

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• Natural actor-critic algorithms
• \({\mathcal Q}\)-learning
• Least squares policy evaluation algorithms with linear function approximation
• An analysis of temporal-difference learning with function approximation
• OnActor-Critic Algorithms
• Efficient Dynamic Computer Simulation of Robotic Mechanisms
• AN ANALYSIS OF EXPERIENCE REPLAY IN TEMPORAL DIFFERENCE LEARNING

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