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This is an implementation of theproximal policy optimization algorithm for the C++ API of Pytorch. It uses a simpleTestEnvironment to test the algorithm. Below is a small visualization of the environment, the algorithm is tested in.

You first need to install PyTorch. For a clean installation from Anaconda, checkout this shorttutorial, or thistutorial, to only install the binaries.

Do

mkdir buildcd buildcmake -DCMAKE_PREFIX_PATH=/absolut/path/to/libtorch ..make

Run the executable with

cd build./train_ppo

To plot the results, run

cd ..python plot.py --online_view --csv_file data/data.csv --epochs 1 10

It should produce something like shown below.

The algorithm can also be used in test mode, once trained. Therefore, run

cd build./test_ppo

To plot the results, run

cd ..python plot.py --online_view --csv_file data/data_test.csv --epochs 1

The results are saved todata/data.csv and can be visualized by runningpython plot.py. Run

python plot.py --help

for help.