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Code for the Paper Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

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rlfamily_cv

Code for reproducing the results in the paper: Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods. Ching-An Cheng*, Xinyan Yan*, Byron Boots. CoRL 2019. (*: equal contribution).

Installation

Tested in Ubuntu 16.04 and Ubuntu 18.04 with python 3.

Install repo and most of the requirements

Prepare python3 virtual environment:

sudo apt-get install python3-pip
sudo pip3 install virtualenv
virtualenv --system-site-packages -p python3 ./venv
source ./venv/bin/activate
pip install --upgrade -r requirements.txt

Install this repo and requirements:

git clone https://github.com/gtrll/rlfamily_cv.git
git checkout develop
pip install --upgrade -r requirements.txt

You may need to run

export PYTHONPATH="{PYTHONPATH}:[the parent folder of this repo]"

Install Dart

The Ubuntu package is too new for PyDart2, so we install it manually.

First install the requirements following the instructions of Install DART from source at https://dartsim.github.io/install_dart_on_ubuntu.html. We compile and install it manually, because PyDart2 only supports Dart before 6.8.

git clone git://github.com/dartsim/dart.git
cd dart
git checkout tags/v6.7.2
mkdir build
cd build
cmake ..
make -j4
sudo make install

Someitmes you may need to link library manually.

echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/lib:/usr/lib:/usr/local/lib" >> ~/.bashrc

Install PyDart2

Installing PyDart2 through pip does not work, so we install it manually.

git clone https://github.com/sehoonha/pydart2.git
cd pydart2
python setup.py build build_ext
python setup.py develop

Install DartEnv

This is a slightly modified version of DartEnv. The changes include:

  • Make nodisplay as default.
  • Add a state property for convenience.
  • Added arguments for physical parameter perturbation.

To install it,

git clone https://github.com/gtrll/dartenv.git
cd dartenv
git checkout cv
pip install -e .[dart]

Run experiments

Firstly, go to the main folder. Run the experiments in the paper for the CartPole task.

python batch_run.py cp cp -r upper nocv st sa new -a rnatgrad

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Code for the Paper Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

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