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It is common to train a neural network by using samples so that it can realize the required input–output characteristics. However, to obtain such samples is difficult or even impossible in some cases. This paper proposes the use of on-line reinforcement learning (RL) algorithms to train adaptive-resonance-theory-based (ART2) neural networks through interaction with environments, namely RL-ART2 neural...
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