NeurIPS 2022accept39 citations
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
Konstantin Sozykin, Andrei Chertkov, Roman Schutski, ANH-HUY PHAN, Andrzej Cichocki, Ivan Oseledets
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.
Black-boxOptimizationReinforcement LearningTensor TrainCross approximationMaximum VolumeQuantized networks
BibTeX
@inproceedings{
sozykin2022ttopt,
title={{TTO}pt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning},
author={Konstantin Sozykin and Andrei Chertkov and Roman Schutski and ANH-HUY PHAN and Andrzej Cichocki and Ivan Oseledets},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=Kf8sfv0RckB}
}