title: Elastic Decision Transformer
summary: 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. Videos are available at: https://kristery.github.io/edt/
paper_authors._id: 64a650289f3b568c2037fd07
paper_authors.name: Yueh-Hua Wu
paper_authors._id: 64a650289f3b568c2037fd08
paper_authors.name: Xiaolong Wang
paper_authors._id: 64a650289f3b568c2037fd09
paper_authors.name: Masashi Hamaya
paper_title: Elastic Decision Transformer
paper_summary: 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. Videos are available at: https://kristery.github.io/edt/
paper_ai_keywords: Decision Transformer (DT) Elastic Decision Transformer (EDT) trajectory stitching optimal trajectory sub-optimal trajectories action inference history length trajectory optimization D4RL locomotion benchmark Atari games