Reinforcement learning and adaptive dynamic programming for feedback control
Frank L. Lewis, Draguna Vrabie · IEEE Circuits and Systems Magazine · 2009
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.