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RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments

Authors:

Brian Tanner,

Adam WhiteAuthors Info & Claims

The Journal of Machine Learning Research, Volume 10

Pages 2133 - 2136

Published: 01 December 2009 Publication History

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Abstract

RL-Glue is a standard, language-independent software package for reinforcement-learning experiments. The standardization provided by RL-Glue facilitates code sharing and collaboration. Code sharing reduces the need to re-engineer tasks and experimental apparatus, both common barriers to comparatively evaluating new ideas in the context of the literature. Our software features a minimalist interface and works with several languages and computing platforms. RL-Glue compatibility can be extended to any programming language that supports network socket communication. RL-Glue has been used to teach classes, to run international competitions, and is currently used by several other open-source software and hardware projects.

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Da Silva FCosta A(2019)A survey on transfer learning for multiagent reinforcement learning systemsJournal of Artificial Intelligence Research10.1613/jair.1.1139664:1(645-703)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11396
François-Lavet VHenderson PIslam RBellemare MPineau J(2018)An Introduction to Deep Reinforcement LearningFoundations and Trends® in Machine Learning10.1561/220000007111:3-4(219-354)Online publication date: 20-Dec-2018
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Schrum JTakadama K(2018)Evolving indirectly encoded convolutional neural networks to play tetris with low-level featuresProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205459(205-212)Online publication date: 2-Jul-2018
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The Journal of Machine Learning Research Volume 10, Issue

12/1/2009

2936 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

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Published: 01 December 2009

Published in JMLR Volume 10

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Da Silva FCosta A(2019)A survey on transfer learning for multiagent reinforcement learning systemsJournal of Artificial Intelligence Research10.1613/jair.1.1139664:1(645-703)Online publication date: 1-Jan-2019
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François-Lavet VHenderson PIslam RBellemare MPineau J(2018)An Introduction to Deep Reinforcement LearningFoundations and Trends® in Machine Learning10.1561/220000007111:3-4(219-354)Online publication date: 20-Dec-2018
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Schrum JTakadama K(2018)Evolving indirectly encoded convolutional neural networks to play tetris with low-level featuresProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205459(205-212)Online publication date: 2-Jul-2018
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Gillespie LGonzalez GSchrum JBosman P(2017)Comparing direct and indirect encodings using both raw and hand-designed features in tetrisProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071195(179-186)Online publication date: 1-Jul-2017
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Geramifard ADann CKlein RDabney WHow J(2015)RLPyThe Journal of Machine Learning Research10.5555/2789272.288679916:1(1573-1578)Online publication date: 1-Jan-2015
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Bianchi RCeliberto LSantos PMatsuura JLopez de Mantaras R(2015)Transferring knowledge as heuristics in reinforcement learningArtificial Intelligence10.1016/j.artint.2015.05.008226:C(102-121)Online publication date: 1-Sep-2015
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Systematic composition of independent language features

Software glue: connecting reusable software components

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