Rlpy: A Value-Function-Based Reinforcement Learning Framework for Education and Research
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How_RLPy.pdf
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475.3 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Dann, Christoph
Dabney, William
Geramifard, Alborz
Klein, Robert Henry
How, Jonathan P.
Date Issued
August 2015
Journal
Journal of Machine Learning Research
Publisher
MIT Press
Citation
Geramifard, Alborz et al. "RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research." Journal of Machine Learning Research 16 (2015):1573−1578.
Version
Final published version
Abstract
RLPy is an object-oriented reinforcement learning software package with a focus on valuefunction-based methods using linear function approximation and discrete actions. The framework was designed for both educational and research purposes. It provides a rich library of fine-grained, easily exchangeable components for learning agents (e.g., policies or representations of value functions), facilitating recently increased specialization in reinforcement learning. RLPy is written in Python to allow fast prototyping, but is also suitable for large-scale experiments through its built-in support for optimized numerical libraries and parallelization. Code profiling, domain visualizations, and data analysis are integrated in a self-contained package available under the Modified BSD License at
http://github.com/rlpy/rlpy. All of these properties allow users to compare various reinforcement learning algorithms with little effort.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://jmlr.org/papers/v16/geramifard15a.html
