This repository is a python implementation of Playground and Mining domain in NeurIPS 2018 Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies.
@inproceedings{sohn2018hierarchical,
title={Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies},
author={Sohn, Sungryull and Oh, Junhyuk and Lee, Honglak},
booktitle={Advances in Neural Information Processing Systems},
pages={7156--7166},
year={2018}
}
- Python 3 (it might work with Python 2, but I didn't test it)
- Pygame, graphviz (for interactive visualization only)
You can perform a minimal installation of SGE with:
git clone https://github.com/srsohn/subtask-graph-maze.git
cd subtask-graph-maze
pip install .
If you want interactive demo with visualization:
pip install .[visualize]
The following command runs interactive demonstration of Playground environment with 'D3' graph set (see the paper):
python demo_visual.py --game_name playground --graph_param D3_eval_1
The following command runs interactive demonstration of Mining environment with 'train' graph set (see the paper):
python demo_visual.py --game_name mining --graph_param train_1
The following command runs demonstration of random policy in Playground environment with 'D2' graph set (see the paper):
python demo_random.py --game_name playground --graph_param D2_eval_1
The following command runs demonstration of random policy in Mining environment with 'eval' graph set (see the paper):
python demo_random.py --game_name mining --graph_param eval_1
The icons used in Mining domain were downloaded from www.flaticon.com.