Planning Using Schrödinger Bridge Diffusion Models   

This repo contains the code for the experiments performed as part of the work Planning Using Schrödinger Bridge Diffusion Models, This repo was originally forked from the Diffuser codebase.

The experiments are based on the Maze2D environment of the D4RL benchmark.

Installation

conda env create -f environment.yml
conda activate diffusion
pip install -e .

Usage

Train a diffusion model with:

python scripts/train.py --config config.maze2d --dataset maze2d-large-v1

The default hyperparameters are listed in config/maze2d.py. You can override any of them with runtime flags, eg --batch_size 64.

Plan using the diffusion model with:

python scripts/plan_maze2d.py --config config.maze2d --dataset maze2d-large-v1

Docker

1. Build the container:

docker build -f azure/Dockerfile . -t diffuser

2. Test the container:

docker run -it --rm --gpus all \
    --mount type=bind,source=$PWD,target=/home/code \
    --mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \
    diffuser \
    bash -c \
    "export PYTHONPATH=$PYTHONPATH:/home/code && \
    python /home/code/scripts/train.py --dataset hopper-medium-expert-v2 --logbase logs/docker"

Reference

@misc{srivastava2024planningusingschrodingerbridge,
      title={Planning Using Schr\"odinger Bridge Diffusion Models}, 
      author={Adarsh Srivastava},
      year={2024},
      eprint={2406.12458},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2406.12458}, 
}