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A curated list of Decision Transformer resources (continually updated)

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Awesome Decision Transformer

This is a collection of research papers for Decision Transformer (DT). And the repository will be continuously updated to track the frontier of DT.

Welcome to follow and star!

Table of Contents

Overview of Transformer

The Decision Transformer was proposed by “Decision Transformer: Reinforcement Learning via Sequence Modeling” by Chen L. et al. It casts (offline) Reinforcement Learning as a conditional-sequence modeling problem.

image info

Specifically, DT model is a causal transformer model conditioned on the desired return, (past) states, and actions to generate future actions in an autoregressive manner.

Advantage

  1. Bypass the need for bootstrapping for long term credit assignment
  2. Avoid undesirable short-sighted behaviors due to the discounting future rewards.
  3. Enjoy the transformer models widely used in language and vision, which are easy to scale and adapt to multi-modal data.

Papers

format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - publisher
  - key 
  - code 
  - experiment environment

Arxiv

NeurIPS 2023

CoRL 2023

  • Q-Transformer
    • Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine
    • Publisher: CoRL 2023
    • Key: Reinforcement Learning, Offline RL, Transformers, Q-Learning, Robotic Manipulation
    • Code: Unofficial
    • ExpEnv: None

ICLR 2023

NeurIPS 2022

CoRL 2022

ICML 2022

AAAI 2022

ICLR 2022

NeurIPS 2021

ICML 2021

Contributing

Our purpose is to make this repo even better. If you are interested in contributing, please refer to HERE for instructions in contribution.

License

Awesome Decision Transformer is released under the Apache 2.0 license.

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A curated list of Decision Transformer resources (continually updated)

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