Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning

Researchers introduce Return Augmented (REAG) method for Decision Transformer frameworks to improve offline reinforcement learning when training data comes from different dynamics than the target domain. The method aligns return distributions between source and target domains, with theoretical analysis showing it achieves optimal performance levels despite dynamics shifts.