Elastic Decision Transformer

Yueh-Hua Wu, Xiaolong Wang, Masashi Hamaya

Keywords: Offline Reinforcement Learning, Trajectory Stitching, Decision Transformer

TL;DR: We propose the Elastic Decision Transformer, an advanced offline Reinforcement Learning algorithm that effectively achieves trajectory stitching and optimizes performance through dynamic adjustment of history length.

Abstract: This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games.

Submission Number: 134