This package provides a loosely-organized set of tools for feature generation using spectral graph theory on state spaces, primarily intended for value function approximation in reinforcement learning.
Craig Corcoran <ccor@cs.utexas.edu>
Bryan Silverthorn <bcs@cargo-cult.org>
This package is implemented in Python. To begin, set up a virtual environment with virtualenv. From the project root (the directory containing this README):
python virtualenv.py --no-site-packages environment
source environment/bin/activate
Then install the minimum set of relevant dependencies:
easy_install numpy
easy_install scipy
easy_install scikit-learn
easy_install plac
The project experiments are organized as modules under the "specmine.experiments" package. To obtain, for example, the Tic-Tac-Toe eigenvector visualization data, run
python -m specmine.experiments.ttt_analyze_evs eigenvectors.csv
Please contact the authors if any questions or difficulties arise.