Train offline Decision Transformer on HalfCheetah via HuggingFace Colab notebook

AI Impact Summary

The article outlines an end-to-end workflow for training an offline Decision Transformer on the MuJoCo HalfCheetah task using a GPT-2–style transformer conditioned on returns, states, and actions. It details data preprocessing (normalization, discounted returns, reward/return scaling) and a custom data collator to sample trajectories, enabling reproducible offline RL experiments within the HuggingFace ecosystem. This creates a low-friction path for engineers to prototype sequence-model RL alternatives without online interaction, though moving to production will require scalable compute and careful handling of MuJoCo licensing and environment compatibility.