The supplementary material includes
1) source codes (train.py and under "erl_lib" directory)
2) text file about how to build an execution environment and commands to reproduce each experiments (README.md)
3) some gif files visualizing the learned policy for each tasks after 500K environment steps (under "assets" directory).