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Robert Babuska
Researcher at Delft University of Technology
Publications - 381
Citations - 17611
Robert Babuska is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Reinforcement learning. The author has an hindex of 56, co-authored 371 publications receiving 15388 citations. Previous affiliations of Robert Babuska include Carnegie Mellon University & Czech Technical University in Prague.
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Experience Replay for Real-Time Reinforcement Learning Control
TL;DR: This paper evaluates ER RL on real-time control experiments that involve a pendulum swing-up problem and the vision-based control of a goalkeeper robot, and develops a general ER framework that can be combined with essentially any incremental RL technique, and instantiate this framework for the approximate Q-learning and SARSA algorithms.
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Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks
TL;DR: The long-short-term memory (LSTM) recurrent neural network is proposed to accomplish fault detection and identification tasks based on the commonly available measurement signals by considering the signals from multiple track circuits in a geographic area.
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Multi-agent discrete-time graphical games and reinforcement learning solutions
TL;DR: A novel reinforcement learning value iteration algorithm is given to solve the dynamic graphical games in an online manner along with its proof of convergence, and it is proved that this notion holds if all agents are in Nash equilibrium and the graph is strongly connected.
Journal Article
Improved covariance estimation for Gustafson-Kessel clustering
TL;DR: In this article, two techniques to improve the calculation of the fuzzy covariance matrix in the Gustafson-Kessel (GK) clustering algorithm are presented, which are useful when the GK algorithm is employed in the extraction of Takagi-Sugeno fuzzy model from data.
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Fuzzy predictive control applied to an air-conditioning system
TL;DR: Comparisons with a nonlinear predictive control scheme based on iterative numerical optimization show that the proposed method requires fewer computations and achieves better performance.