| 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 |