Deep Reinforcement Learning for a Dictionary Based Compression Schema (Student Abstract)

Keren Nivasch, Dana Shapira, Amos Azaria

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An increasingly important process of the internet age and the massive data era is file compression. One popular compression scheme, Lempel-Ziv-Welch (LZW), maintains a dictionary of previously seen strings. The dictionary is updated throughout the parsing process by adding new encountered substrings. Klein, Opalinsky and Shapira (2019) recently studied the option of selectively updating the LZW dictionary. They show that even inserting only a random subset of the strings into the dictionary does not adversely affect the compression ratio. Inspired by their approach, we propose a reinforcement learning based agent, RLZW, that decides when to add a string to the dictionary. The agent is first trained on a large set of data, and then tested on files it has not seen previously (i.e., the test set). We show that on some types of input data, RLZW outperforms the compression ratio of a standard LZW.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages15857-15858
Number of pages2
ISBN (Electronic)9781713835974
StatePublished - 2021
Externally publishedYes
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume18

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

Bibliographical note

Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Funding

This work was supported by the Ministry of Science & Technology, Israel and by the Data Science and Artificial Intelligence Center of Ariel University.

FundersFunder number
Data Science and Artificial Intelligence Center of Ariel University
Ministry of science and technology, Israel

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