AI BriefingDecision Transformer Learning Guide
Key point
Introduces the concept and training method of Decision Transformer, which solves reinforcement learning as sequence modeling.
Details
Decision Transformer presents a new paradigm that redefines and solves reinforcement learning (RL) as a conditional-sequence modeling problem.
While existing RL optimizes a value function to maximize reward, Decision Transformer adopts a generative trajectory modeling approach that uses a Transformer to take a sequence of target reward (Return-to-go), past states, and actions as input and generate future actions.
The key features and training method are as follows:
- Input structure: For the last K timesteps, three elements—Return-to-go, State, and Action—are used as input.
- Model architecture: The input tokens go through an embedding process and are passed to a GPT-2 model, which predicts future actions autoregressively through causal self-attention.
- Hands-on content: Using Hugging Face's
transformerslibrary and theTrainerAPI, the guide covers the process of training a model from scratch based on an offline dataset from the HalfCheetah environment.
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