
Decision Transformer
Overview
The Decision Transformer model was proposed in Decision Transformer: Reinforcement Learning via Sequence Modeling
by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
The abstract from the paper is the following:
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. 
This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances
 in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that 
 casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or 
 compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked 
 Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our 
 Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, 
 Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on 
 Atari, OpenAI Gym, and Key-to-Door tasks.
This version of the model is for tasks where the state is a vector.
This model was contributed by edbeeching. The original code can be found here.
DecisionTransformerConfig
[[autodoc]] DecisionTransformerConfig
DecisionTransformerGPT2Model
[[autodoc]] DecisionTransformerGPT2Model
    - forward
DecisionTransformerModel
[[autodoc]] DecisionTransformerModel
    - forward