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PIG

Implementation of Imitating Graph-Based Planning with Goal-Conditioned Policies (ICLR 2023) in PyTorch.

Our code is based on official implementation of Mapping State Space.

Instructions

Install dependencies

conda create -n pig python=3.6
conda activate pig
conda install pytorch=1.3.1 cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt

Experiments

To reproduce our experiments, please run below scripts

2D Reach

source ./scripts/train_2dplane.sh {GPU} {SEED}

Ant Maze

source ./scripts/train_antmaze.sh AntMazeL v1 {GPU} {SEED}  # L-shape
source ./scripts/train_antmaze.sh AntMaze v1 {GPU} {SEED}  # U-shape
source ./scripts/train_antmaze.sh AntMaze v0 {GPU} {SEED}  # Large U-shape
source ./scripts/train_antmaze.sh AntMazeS v1 {GPU} {SEED}  # S-shape
source ./scripts/train_antmaze.sh AntMazeW v1 {GPU} {SEED}  # W-shape
source ./scripts/train_antmaze.sh AntMazeP v1 {GPU} {SEED}  # Pi-shape

Pusher

source ./scripts/train_pusher.sh {GPU} {SEED}

Reacher

source ./scripts/train_reacher.sh {GPU} {SEED}

If you find this code useful, please reference in our paper:

@inproceedings{kim2023imitating,
  title={Imitating Graph-Based Planning with Goal-Conditioned Policies},
  author={Junsu Kim and Younggyo Seo and Sungsoo Ahn and Kyunghwan Son and Jinwoo Shin},
  booktitle={International Conference on Learning Representations},
  year={2023},
  url={https://openreview.net/forum?id=6lUEy1J5R7p}
}