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

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.