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Reinforcement learning in associative memory

A reinforcement learning based associative memory structure (RLAM) is proposed. In this structure, a one-layer feed forward Palm [Palm, G., 1980] model is applied to the networks. Instead of batch training, an on-line learning method is used to construct the memory. The networks are trained interactively according to reinforcement learning, which is biologically plausible. The experiment results show that the networks converge and generalize well.