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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.