Reinforcement learning and adaptive dynamic programming for feedback control. Lewis, F. L. & Vrabie, D. IEEE Circuits and Systems Magazine, 9(3):32–50, 2009. ![Reinforcement learning and adaptive dynamic programming for feedback control [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This actionbased or Reinforcement Learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for Reinforcement Learning and a practical implementation method known as Adaptive Dynamic Programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.
@article{lewis_reinforcement_2009,
title = {Reinforcement learning and adaptive dynamic programming for feedback control},
volume = {9},
issn = {1531-636X, 1558-0830},
url = {https://ieeexplore.ieee.org/document/5227780/},
doi = {10.1109/mcas.2009.933854},
abstract = {Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This actionbased or Reinforcement Learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for Reinforcement Learning and a practical implementation method known as Adaptive Dynamic Programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.},
language = {en},
number = {3},
urldate = {2022-02-03},
journal = {IEEE Circuits and Systems Magazine},
author = {Lewis, Frank L. and Vrabie, Draguna},
year = {2009},
keywords = {/unread},
pages = {32--50},
}
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