TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement LearningDownload PDF

Published: 31 Oct 2022, Last Modified: 12 Oct 2022NeurIPS 2022 AcceptReaders: Everyone
Keywords: Black-box, Optimization, Reinforcement Learning, Tensor Train, Cross approximation, Maximum Volume, Quantized networks
Abstract: We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the applicability of the new Tensor Train Optimizer (TTOpt) method for various tasks, ranging from minimization of multidimensional functions to reinforcement learning. Our algorithm compares favorably to popular gradient-free methods and outperforms them by the number of function evaluations or execution time, often by a significant margin.
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