/taxons

Task Agnostic eXploration of Outcome spaces through Novelty and Surprise

Primary LanguagePython

TAXONS

Task Agnostic eXploration of Outcome spaces through Novelty and Surprise.

This is the code of the paper: Unsupervised Learning and Exploration of Reachable Outcome Space

To install run:

pipenv shell --three
python setup.py install

We also provide a containerized version running in Singularity at: https://github.com/GPaolo/taxons_sif

Dependencies

NB: if you're using the virtualenv, activate it before installing the dependencies.

Pybullet Gym

I am using a slightly modified version of pybulletgym than the original found here: https://github.com/benelot/pybullet-gym.

To install it, activate the virtual env, go in the external folder and run:

git clone https://github.com/GPaolo/pybullet-gym.git
cd pybullet-gym
pip install -e .

If you want more informations, look at the README there.

Fastsim

TAXONS needs Fastsim to be installed. I am using a slightly modified version of the original.

To install it we first need to download Pyfastsim:

git clone https://github.com/alexendy/pyfastsim

Now we need to install libfastsim in pyfastsim. To do so, execute:

cd external/pyfastsim
git clone https://github.com/GPaolo/libfastsim.git
cd libfastsim
git checkout patch-1
git pull origin patch-1
patch -p1 < ../fastsim-boost2std-fixdisplay.patch
./waf configure
./waf build
./waf install

NB If it complains that cannot find boost, then install it by running:

sudo apt-get install libboost-all-dev

Now we can install pyfastsim by running, in the external/pyfastsim folder:

python setup.py install

Finally we can install fastsim-gym. To do so, activate the virtual env and enter the external folder. Then do:

cd external
git clone https://github.com/GPaolo/fastsim_gym.git
git checkout patch-1
git pull origin patch-1
python setup.py install

The file .env will be loaded automatically with pipenv shell or pipenv run your_command and the environment variables will be available.

NB: within Pycharm you need the plugin Env File to load it automatically (access Env File tab from the Run/Debug configurations). You will have to run PyCharm from the shell itself from inside the activated virtualenv

Running

To run the algorithm you just need to launch:

python scripy/train.py

If you want to change the experiment parameters, go to: script/parameters.py

To plot the results, just run:

python scripts/plot.py