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A Reinforcement-Learning Neural Network for the Control of Nonlinear Systems

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1 Author(s)
Kevin L. Moore ; Measurements and Control Research Center, College of Engineering, Box 8060, Idaho State University, Pocatello, Idaho 83209-0009

Reinforcement learning in neural nets is an approach to the problem of credit assignment during learning. As opposed to gradient descent techniques such as backpropagation, a reinforcement learning scheme uses a single reinforcement signal from the environment to adjust the network weights. In this short paper we describe reinforcement learning and propose a multilayer neural network with real-valued outputs which learns using a combination of reinforcement learning and backpropagation. This method combines several ideas from the literature. We illustrate the use of the method with an example of the control of a nonlinear system.

Published in:

American Control Conference, 1991

Date of Conference:

26-28 June 1991