Neural networks for control by W Thomas Miller; Richard S Sutton; Paul J Werbos; National PDF

By W Thomas Miller; Richard S Sutton; Paul J Werbos; National Science Foundation (U.S.)

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This principle is to choose the class of models for system identification partly on the basis of the existence of specialized techniques applicable to the models in the class. e. , time-invariant linear systems ) . When a model is specified by the parameter estimation process, one can design a controller under the assumption that this model is an accurate model of the plant. In the same way, determining a plant model in the form of a layered network with differentiable squashing functions allows one to apply the backpropagation process to estimate how inputs to the plant should be altered to change the plant's output in desired ways.

In this case, a negative evaluation may mean that system performance is not as good as could be expected based on past experience with similar situations. In this case, the signal is a kind of derivative, but it is not a derivative with respect to actions, and its being negative does not in itself indicate how the action should be altered (indeed, the action will generally be a vector) . A single evaluation of an action does not contain information about how to beneficially change that action. There are, however, many ways to estimate the required gradient information on the basis of evaluation signals, each of which produces a different type of reinforcement-learning algorithm.

Le Cun ( 1988) has pointed out that the backpropagation method can be derived using a Lagrangian formulation like the one used in optimal control. It turns out that algorithms similar to backpropagation are used in optimal control-although not for adjusting the parameters of a system model. It is interesting that the method described here for using backpropagation to differentiate a model for control purposes is more closely related to optimal control methods than is its application to parameter estimation for system identification ( see, for example, Bryson and Ho 1969) .

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