{"title":"LiteEFG: An Efficient Python Library for Solving Extensive-form Games","authors":"Mingyang Liu, Gabriele Farina, Asuman Ozdaglar","doi":"arxiv-2407.20351","DOIUrl":null,"url":null,"abstract":"LiteEFG is an efficient library with easy-to-use Python bindings, which can\nsolve multiplayer extensive-form games (EFGs). LiteEFG enables the user to\nexpress computation graphs in Python to define updates on the game tree\nstructure. The graph is then executed by the C++ backend, leading to\nsignificant speedups compared to running the algorithm in Python. Moreover, in\nLiteEFG, the user needs to only specify the computation graph of the update\nrule in a decision node of the game, and LiteEFG will automatically distribute\nthe update rule to each decision node and handle the structure of the\nimperfect-information game.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computer Science and Game Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.20351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
LiteEFG is an efficient library with easy-to-use Python bindings, which can
solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to
express computation graphs in Python to define updates on the game tree
structure. The graph is then executed by the C++ backend, leading to
significant speedups compared to running the algorithm in Python. Moreover, in
LiteEFG, the user needs to only specify the computation graph of the update
rule in a decision node of the game, and LiteEFG will automatically distribute
the update rule to each decision node and handle the structure of the
imperfect-information game.