PCDT

This is the official Pytorch implementation for the paper:

Predictive Coding for Decision Transformer

Tung M. Luu*, Donghoon Lee*, Chang D. Yoo

*equal contribution

A link to our paper can be found on : https://arxiv.org/abs/2410.03408

Installation

To install requirements:

pip install git+https://github.com/Farama-Foundation/d4rl@master#egg=d4rl
pip install git+https://github.com/tunglm2203/pcdt
conda create -n pcdt python=3.7
codna activate pcdt
pip install -r requirements.txt
pip install -e .

Data preprocessing:

python -m pcdt.data.parse_d4rl

First Stage : Predictive Coding Learning

Antmaze

./scripts\PCDT\launch_pcdt_trl_antmaze_stategoal.sh

Kitchen

./scripts\PCDT\launch_pcdt_trl_kitchen_stategoal.sh

Second Stage : Policy Learning (DT) based on Predictive Coding

Antmaze

./scripts\PCDT\launch_DT_antmaze.sh

Kitchen

./scripts\PCDT\launch_DT_kitchen.sh

The code will be updated further in the near future.

Citation

If you use this repo in your research, please consider citing the paper as follows

@article{luu2024predictive,
  title={Predictive Coding for Decision Transformer},
  author={Luu, Tung M and Lee, Donghoon and Yoo, Chang D},
  journal={arXiv preprint arXiv:2410.03408},
  year={2024}
}

Acknowledgements

This code is based on top of : Decision Transformer , Goal-Conditioned Predictive Coding for Offline Reinforcement Learning , Masked Trajectory Models.