Learning to Control Dynamic Systems Via Associative Reinforcement Learning

Learning to Control Dynamic Systems Via Associative Reinforcement Learning

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by Vijaykumar Gullapalli

BibTeX

@MISC{Gullapalli_learningto,
    author = {Vijaykumar Gullapalli},
    title = {Learning to Control Dynamic Systems Via Associative Reinforcement Learning},
    year = {}
}

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Abstract

this paper. The internal critic network has 8 input units, a hidden layer of 10 back-propagation units, and a single temporal difference output unit. The controller has 4 input units and a single action unit. In simulations of the supervised learning method, a "noisy" linear unit was used as the action unit, while in simulations of the reinforcement learning method, a stochastic real-valued (SRV) unit [25] was used.

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