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）及其变体的重大改进。尽管DT声称能够生成最优轨迹，但实验证据表明，它在轨迹拼接方面存在困难——轨迹拼接是指从一组次优轨迹的最优部分中生成最优或近似最优轨迹的过程。所提出的EDT通过调整DT中保留的历史长度，在测试时的动作推理阶段实现轨迹拼接，从而区别于现有方法。此外，EDT通过在前一轨迹最优时保留较长历史、次优时保留较短历史来优化轨迹，使其能够与更优轨迹进行“拼接”。广泛实验表明，EDT能够弥合基于DT的方法与基于Q学习的强化学习方法之间的性能差距。具体而言，在D4RL运动基准测试和Atari游戏的多任务场景中，EDT的表现优于基于Q学习的方法。视频演示请访问：https://kristery.github.io/edt/