BC Baseline on D4RL

Key Insight

Behavior cloning is the most basic thing you can do with a fixed dataset of past experience: throw away the rewards, treat each recorded state as an input and the action the dataset's behavior policy took as the correct label, and train a policy by plain supervised learning to reproduce those actions. Run on a D4RL task like walker2d-medium-v2 — a Walker2d locomotion dataset — it gives you a return number that every fancier offline RL method must beat to justify its complexity. Why it matters: when the dataset was collected by a strong expert, simply copying it is hard to beat, so imitation learning by BC is the honest yardstick you always measure first — skip it and you risk celebrating a complicated algorithm that never actually outperformed "just copy the data."