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Probabilistic Differential Dynamic Programming (PDDP) is a data-driven,probabilistic trajectory optimization framework for systems with unknowndynamics. This is an implementation of Yunpeng Pan and Evangelos A. Theodorou'spaper inPyTorch,[1].

This is a work in progress and does not work/converge as is yet.

To install simply clone and run:

pip install.

You may also install the dependencies with pipenv as follows:

pipenv install

Finally, you may add this to your own application with either:

pip install'git+https://github.com/anassinator/pddp.git#egg=pddp'pipenv install'git+https://github.com/anassinator/pddp.git#egg=pddp'

After installing,import as follows:

importpddp

You can see thenotebooks directory forJupyter notebooks to see how common control problemscan be solved through PDDP.

Contributions are welcome. Simply open an issue or pull request on the matter.

You can run all unit tests and benchmarks throughpytestas follows:

pytest

To speed things up, you may also run tests in parallel and disable benchmarkswith:

pytest -n auto --benchmark-disable

You can installpytest with:

pipenv install --dev

We useYAPF for all Python formatting needs.You can auto-format your changes with the following command:

yapf --recursive --in-place --parallel.

You can install the formatter with:

pipenv install --dev

SeeLICENSE.