Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations

• URL: http://arxiv.org/abs/2201.02571v1
• Date: Fri, 7 Jan 2022 18:09:23 GMT
• Title: Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations
• Authors: Dmitry Ivanov, Mikhail Kiselev, and Denis Larionov
• Abstract summary: This article proposes a sparse computation-based method for optimizing neural networks for reinforcement learning tasks. It significantly reduces the number of multiplications when running neural networks.
• Score: 3.4328283704703866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This article proposes a sparse computation-based method for optimizing neural networks for reinforcement learning (RL) tasks. This method combines two ideas: neural network pruning and taking into account input data correlations; it makes it possible to update neuron states only when changes in them exceed a certain threshold. It significantly reduces the number of multiplications when running neural networks. We tested different RL tasks and achieved 20-150x reduction in the number of multiplications. There were no substantial performance losses; sometimes the performance even improved.

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