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/

翻译：本文提出弹性决策Transformer（EDT），这是对现有决策Transformer（DT）及其变体的重要改进。尽管DT声称能生成最优轨迹，但实证证据表明其难以处理轨迹拼接——即通过组合一系列次优轨迹的最优片段来生成最优或接近最优轨迹的过程。本文提出的EDT通过在测试时动作推理阶段调整DT中维护的历史长度来实现轨迹拼接，从而脱颖而出。此外，EDT通过保留更长的历史记录（当前轨迹为最优时）或更短的历史记录（当前轨迹为次优时）来优化轨迹，使其能够"拼接"出更优轨迹。大量实验表明，EDT能够弥合基于DT的方法与基于Q学习的方法之间的性能差距。特别是在D4RL运动基准和Atari游戏的多任务场景中，EDT优于基于Q学习的方法。视频演示见：https://kristery.github.io/edt/