Decision-Transformer-based Satisficing Intelligent Agent

A version of Decision Transformer (see below) modified to satisfice, including Expectation Updating along the trajectory.

Original code contributors: see below.

Modifications: Jobst Heitzig

Decision Transformer

Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor Mordatch†

*equal contribution, †equal advising

A link to our paper can be found on arXiv.

Overview

Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling. Contains scripts to reproduce experiments.

Instructions

We provide code in two sub-directories: atari containing code for Atari experiments and gym containing code for OpenAI Gym experiments. See corresponding READMEs in each folder for instructions; scripts should be run from the respective directories. It may be necessary to add the respective directories to your PYTHONPATH.

Citation

Please cite our paper as:

@article{chen2021decisiontransformer,
  title={Decision Transformer: Reinforcement Learning via Sequence Modeling},
  author={Lili Chen and Kevin Lu and Aravind Rajeswaran and Kimin Lee and Aditya Grover and Michael Laskin and Pieter Abbeel and Aravind Srinivas and Igor Mordatch},
  journal={arXiv preprint arXiv:2106.01345},
  year={2021}
}

Note: this is not an official Google or Facebook product.

License

MIT