Installation

• Simulations were run on python 3.6 (and 3.7) with pytorch 1.7.1 (both with and without GPU)
• Other Python Simulation Dependencies: numpy, gym, pyyaml, pybullet, mujoco-py, roboschool
• Python Visualization Dependencies: matplotlib, ipython, jupyterlab, seaborns

Environments Modifications

• Some versions of the roboschool environments do not properly initialize the swingup environment. If you run the RoboschoolInvertedPendulumSwingup environment, and it appears to be doing the balance task, check roboschool/gym_pendulums.py the __init__() function for the RoboschoolInvertedPendulumSwingup task. The __init__() function should include RoboschoolInvertedPendulum.__init__(self,swingup=True)
• The gym Acrobot environment was modified to test in the continuous space and the reward function was updated to include a dependency on the action and the state. The details can be viewed in /envs/continuous_acrobot.py

Copyright and License

The implementations of Hybrid Learning contained herein are copyright (C) 2021 - 2022 by Allison Pinosky and Todd Murphey, and are distributed under the terms of the GNU General Public License (GPL) version 3 (or later). Please see the LICENSE for more information.

Contact: apinosky@u.northwestern.edu

Lab Info: Todd D. Murphey https://murpheylab.github.io/ Northwestern University