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Reinforcement learning and adaptive dynamic programming for feedback control

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2 Author(s)
Frank L. Lewis ; University of Texas at Arlington, TX, USA ; Draguna Vrabie

Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based 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.

Published in:

IEEE Circuits and Systems Magazine  (Volume:9 ,  Issue: 3 )