CITATION.cff

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cff-version: 1.2.0
title: Riemannian-PO-for-LQG
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Spencer
    family-names: Kraisler
    email: spencerkraisler@gmail.com
    affiliation: University of Washington
    orcid: 'https://orcid.org/0009-0009-4674-0104'
  - given-names: Mehran
    family-names: Mesbahi
    email: mesbahi@uw.edu
    affiliation: University of Washington
repository-code: 'https://github.com/Rainlabuw/Riemannian-PO-for-LQG'
abstract: >-
  This is an implementation of my paper Dynamic
  Output-feedback Synthesis Orbit Geometry: Quotient
  Manifolds and LQG Direct Policy Optimization by Kraisler
  and Mesbahi.


  This repo is an implementation of gradient descent (GD) on
  the Linear-Quadratic-Gaussian (LQG) cost. It also performs
  Riemannian gradient descent (RGD) with respect to the
  Krishnaprasad-Martin (KM) metric introduced in the above
  paper.
keywords:
  - machine-learning
  - optimization
  - optimal-control
  - policy-optimization