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Deep reinforcement learning with significant multiplications inference - Scientific Reports
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We propose a sparse computation method for optimizing the inference of neural networks in reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this method combines simple neural network pruning with a delta-network algorithm to account for the input data correlations. The former mimics neuroplasticity by eliminating inefficient connections; the latter makes it possible to update neuron states only when their changes exceed a certain threshold.
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