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

Autor:	Sozykin, Konstantin, Chertkov, Andrei, Schutski, Roman, Phan, Anh-Huy, Cichocki, Andrzej, Oseledets, Ivan
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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 evolutionary-based methods and outperforms them by the number of function evaluations or execution time, often by a significant margin. Comment: 26 pages, 8 figures, accepted to Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). Pre camera-ready version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.00293 Zobrazit plný text záznamu View this record from Arxiv