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OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems
This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302566/ http://dx.doi.org/10.1007/978-3-030-50426-7_33 |
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author | Zheng, Weijian Wang, Dali Song, Fengguang |
author_facet | Zheng, Weijian Wang, Dali Song, Fengguang |
author_sort | Zheng, Weijian |
collection | PubMed |
description | This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions. |
format | Online Article Text |
id | pubmed-7302566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73025662020-06-19 OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems Zheng, Weijian Wang, Dali Song, Fengguang Computational Science – ICCS 2020 Article This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions. 2020-05-25 /pmc/articles/PMC7302566/ http://dx.doi.org/10.1007/978-3-030-50426-7_33 Text en © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Zheng, Weijian Wang, Dali Song, Fengguang OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title | OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title_full | OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title_fullStr | OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title_full_unstemmed | OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title_short | OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems |
title_sort | opengraphgym: a parallel reinforcement learning framework for graph optimization problems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302566/ http://dx.doi.org/10.1007/978-3-030-50426-7_33 |
work_keys_str_mv | AT zhengweijian opengraphgymaparallelreinforcementlearningframeworkforgraphoptimizationproblems AT wangdali opengraphgymaparallelreinforcementlearningframeworkforgraphoptimizationproblems AT songfengguang opengraphgymaparallelreinforcementlearningframeworkforgraphoptimizationproblems |