A grey evaluation function for reinforcement learning

A self-organizing control mechanism with a capability of reinforcement learning is proposed. The method is realized by a reinforcement signal predictor based on the grey theory and a policy learning unit implemented by a neural network. In consideration of the stability problem in learning, temporal difference algorithm is used as the weight-update rule of the connectionist. From the results of the simulations and experiments, the proposed method demonstrates that a control task can be learned even with very little a priori knowledge.