Train your first Decision Transformer

Introduces Decision Transformers as a novel approach to Reinforcement Learning by framing it as a conditional-sequence modeling problem solvable with Transformers. Details the architecture, including modality-specific embeddings and GPT-2 processing for autoregressive action prediction. Provides a practical guide to training an offline Decision Transformer from scratch using the 🤗 Trainer and a custom data collator, demonstrating its application on the HalfCheetah environment. Includes code snippets for dataset loading, custom data collator implementation (including state normalization, return discounting, and trajectory length sampling), and model training setup.