Awesome

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Paper Link : https://arxiv.org/abs/2304.10573

Check out https://github.com/philippe-eecs/JaxDDPM for an implementation of DDPMs in JAX for continuous spaces!

Reproducing Results

Offline Script Location.

Run Line for each variant. Edit the script location above to change hyperparameters and environments to sweep over.

python3 launcher/examples/train_ddpm_iql_offline.py --variant 0...N

Finetune Script Location.

Run

python3 launcher/examples/train_ddpm_iql_finetune.py --variant 0...N

Important File Locations

Main run script were variant dictionary is passed.

DDPM Implementation.

LN_Resnet.

DDPM IQL Learner.

Installation

Run

pip install --upgrade pip
pip install -r requirements.txt
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

See instructions for other versions of CUDA here.

Based from a re-implementation of https://github.com/ikostrikov/jaxrl

Citations

Cite this paper

@misc{hansenestruch2023idql,
      title={IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies}, 
      author={Philippe Hansen-Estruch and Ilya Kostrikov and Michael Janner and Jakub Grudzien Kuba and Sergey Levine},
      year={2023},
      eprint={2304.10573},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Please also cite the JAXRL repo as well if you use this repo

@misc{jaxrl,
  author = {Kostrikov, Ilya},
  doi = {10.5281/zenodo.5535154},
  month = {10},
  title = {{JAXRL: Implementations of Reinforcement Learning algorithms in JAX}},
  url = {https://github.com/ikostrikov/jaxrl},
  year = {2021}
}