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+ **Update on Exudyn V1.8.52**: newer examples use ``exudyn.graphics`` instead of ``GraphicsData`` functions. The old models are backwards-compatible, but the updated examples and test models require version 1.8.52 or newer! Install with ``pip install exudyn --pre -U``
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+ **Update on Exudyn V1.9.0**: newer examples use ``exudyn.graphics`` instead of ``GraphicsData`` functions. The old models are backwards-compatible, but the new updated examples and test models require version 1.8.52 or newer! FEM now uses internally in mass and stiffness matrices the scipy sparse csr matrices, check also your models for that!
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+ **NOTE**: for pure installation, use **pip install exudyn** (see further description below)
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+ *free, open source* and with plenty of *documentation* and *examples*
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|pic1| |pic2| |pic3| |pic4| |pic5| |pic6|
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Johannes Gerstmayr. Exudyn -- A C++ based Python package for flexible multibody systems. Multibody System Dynamics, 2023. `https://doi.org/10.1007/s11044-023-09937-1 <https://doi.org/10.1007/s11044-023-09937-1>`_
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How to cite:
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+ Johannes Gerstmayr. Exudyn -- A C++ based Python package for flexible multibody systems. Multibody System Dynamics, Vol. 60, pp. 533-561, 2024. `https://doi.org/10.1007/s11044-023-09937-1 <https://doi.org/10.1007/s11044-023-09937-1>`_
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Due to limitations for complex formulas, images and references in .rst files, some (small) details are only available in theDoc.pdf, see the `github page of Exudyn <https://github.com/jgerstmayr/EXUDYN/blob/master/docs/theDoc/theDoc.pdf>`_! There may also be some conversion errors in the auto-generated html pages.
Copy file name to clipboardExpand all lines: docs/RST/Examples/openAIgymNLinkContinuous.rst
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@@ -133,7 +133,7 @@ You can view and download this file on Github: `openAIgymNLinkContinuous.py <htt
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masscart =1.
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massarm =0.1
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total_mass = massarm + masscart
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armInertia =self.length**2*0.5*massarm
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armInertia =self.length**2*0.5*massarm#for a rod with equally distributed mass, correctly, it would read self.length**2*massarm/3; using here the values of previous research
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# environment variables and force magnitudes and are taken from the
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# paper "Reliability evaluation of reinforcement learning methods for
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ifself.nLinks ==3:
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self.force_mag =self.force_mag*1.5
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thresholdFactor =2
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ifself.nLinks ==4:
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self.force_mag =self.force_mag*3
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thresholdFactor =5
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ifself.nLinks >=4:
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self.force_mag =self.force_mag*2.5
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thresholdFactor =2.5
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ifself.flagContinuous:
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self.force_mag *=2#continuous controller can have larger max value
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