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Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control
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DOI：10.1109/MCAS.2009.933854.png)
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Abstract
En 中文
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.
Keywords:
NEURAL-NETWORK
CONTINUOUS-TIME
CONVERGENCE
NEUROCONTROL
SYSTEMS
REWARD
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