{"title":"Graph Representations for Reinforcement Learning","authors":"Esteban Schab, Carla Casanova, Fabiana Piccoli","doi":"10.24215/16666038.24.e03","DOIUrl":null,"url":null,"abstract":"Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of low-dimensional vector representations that are more suited for machine learning algorithms, while retaining essential graph features. This paper presents a framework for evaluating graph embedding algorithms and their ability to preserve the structure and relevant features of graphs by means of an internal validation metric, without resorting to subsequent tasks that require labels for training. Based on this framework, three defined algorithms that meet the necessary requirements for solving a specific problem of Reinforcement Learning in graphs are selected, analyzed, and compared. These algorithms are Graph2Vec, GL2Vec, and Wavelet Characteristics, with the latter two demonstrating superior performance.","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Science and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.24215/16666038.24.e03","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of low-dimensional vector representations that are more suited for machine learning algorithms, while retaining essential graph features. This paper presents a framework for evaluating graph embedding algorithms and their ability to preserve the structure and relevant features of graphs by means of an internal validation metric, without resorting to subsequent tasks that require labels for training. Based on this framework, three defined algorithms that meet the necessary requirements for solving a specific problem of Reinforcement Learning in graphs are selected, analyzed, and compared. These algorithms are Graph2Vec, GL2Vec, and Wavelet Characteristics, with the latter two demonstrating superior performance.
期刊介绍:
Journal of Computer Science and Technology (JCST), the first English language journal in the computer field published in China, is an international forum for scientists and engineers involved in all aspects of computer science and technology to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. While the journal emphasizes the publication of previously unpublished materials, selected conference papers with exceptional merit that require wider exposure are, at the discretion of the editors, also published, provided they meet the journal''s peer review standards. The journal also seeks clearly written survey and review articles from experts in the field, to promote insightful understanding of the state-of-the-art and technology trends.
Topics covered by Journal of Computer Science and Technology include but are not limited to:
-Computer Architecture and Systems
-Artificial Intelligence and Pattern Recognition
-Computer Networks and Distributed Computing
-Computer Graphics and Multimedia
-Software Systems
-Data Management and Data Mining
-Theory and Algorithms
-Emerging Areas