Abstract
For learning in continuous value space, reinforcement learning used neural networks is investigated actively. But network configurations of conventional neural network models are fixed, and it is almost impossible to predict and determine proper configuration for best learning performance, because, in reinforcement learning, quantities of training data to obtain enough learning performance are unknown. In this study, we propose a reinforcement learning introduced neuron addition mechanism which is based on RCE model, and evaluate its basical performance.
