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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
title: "KoopmanRL: Koopman-Assisted Reinforcement Learning"
version: 1.0.0
date-released: 2026-04-20
authors:
- family-names: Paehler
given-names: Ludger
email: ludger.paehler@tum.de
affiliation: Technical University of Munich
orcid: "https://orcid.org/0000-0002-7200-7637"
- family-names: Rozwood
given-names: Preston
email: pwr@cornell.edu
affiliation: Cornell University
orcid: "https://orcid.org/0009-0007-3589-3514"
- family-names: Mehrez
given-names: Edward J.
email: ejm322@cornell.edu
affiliation: Cornell University
- family-names: Wen
given-names: Sun
email: ws455@cornell.edu
affiliation: Cornell University
- family-names: Brunton
given-names: Steven L.
email: sbrunton@uw.edu
affiliation: University of Washington
orcid: "https://orcid.org/0000-0002-6565-5118"
repository-code: "https://github.com/dynamicslab/koopmanrl"
url: "https://github.com/dynamicslabs/koopmanrl"
abstract: >
KoopmanRL is a reinforcement learning framework which utilizes the
Koopman operator to enhance maximum entropy-based reinforcement
learning algorithms at the example of the discrete value iteration,
and the Soft actor-critic algorithm. This package accompanies the
paper "Koopman-Assisted Reinforcement Learning" providing the
CleanRL-style implementations of the algorithms, and the hyperparameter
tuning harnesses to tune the algorithms on new environments.
keywords:
- reinforcement learning
- Koopman operator
- PyTorch
- machine learning
license: MIT
references:
- type: journal-paper
title: "Koopman-Assisted Reinforcement Learning"
journal:
name: "tbd"
year: 2026
authors:
- family-names: Rozwood
given-names: Preston
orcid: "https://orcid.org/0009-0007-3589-3514"
- family-names: Mehrez
given-names: Edward J.
- family-names: Paehler
given-names: Ludger
orcid: "https://orcid.org/0000-0002-7200-7637"
- family-names: Wen
given-names: Sun
- family-names: Brunton
given-names: Steven L.
orcid: "https://orcid.org/0000-0002-6565-5118"