This is a Python implementation of the Least Squares Policy Iteration (LSPI) reinforcement learning algorithm. For more information on the algorithm please refer to the paper
“Least-Squares Policy Iteration.”
Lagoudakis, Michail G., and Ronald Parr.
Journal of Machine Learning Research 4, 2003.
https://www.cs.duke.edu/research/AI/LSPI/jmlr03.pdf
You can also visit their website where more information and a Matlab version is provided.
http://www.cs.duke.edu/research/AI/LSPI/
The requirements.txt file contains the python module requirements to use this library, run the tests, and generate the docs. To install all of the listed requirements automatically you can use the command
pip install -r requirements.txt
If you have nosetests you can run the tests with nosetests --config=setup.cfg lspi_testsuite.
If you have virtual environment installed you can run make test which will automatically create a virtual environment
with all of the dependencies and then run the tests.
To generate the docs you will need sphinx. If you have virtual environment installed you can run
make html-docs. This will automatically create a virtual environment with all of the dependencies
and then run sphinx. The output will exist in docs/build/html/.