Abstract
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.
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