This is a PyTorch implementation for our paper "Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning" (NeurIPS 2020 spotlight).
- Python 3.6
- PyTorch 1.3
- OpenAI Gym
- MuJoCo
Also, to run the MuJoCo experiments, a license is required (see here).
Update: implementation for discrete control tasks is in the discrete/ folder; please refer to the usage therein.
- Ant Gather
python main.py --env_name AntGather
- Ant Maze
python main.py --env_name AntMaze
- Ant Maze Sparse
python main.py --env_name AntMazeSparse
- Ant Gather
python eval.py --env_name AntGather --model_dir [MODEL_DIR]
- Ant Maze
python eval.py --env_name AntMaze --model_dir [MODEL_DIR]
- Ant Maze Sparse
python eval.py --env_name AntMazeSparse --model_dir [MODEL_DIR]
Default model_dir is pretrained_models/.
See pretrained_models/ for pre-trained models on all tasks. The expected performances of the pre-trained models are as follows (averaged over 100 evaluation episodes):
| Ant Gather | Ant Maze | Ant Maze Sparse |
|---|---|---|
| 3.0 | 96% | 89% |
If you find this work useful in your research, please cite:
@inproceedings{zhang2020generating,
title={Generating adjacency-constrained subgoals in hierarchical reinforcement learning},
author={Zhang, Tianren and Guo, Shangqi and Tan, Tian and Hu, Xiaolin and Chen, Feng},
booktitle={NeurIPS},
year={2020}
}