As a data-driven paradigm, offline reinforcement learning (RL) has been
formulated as sequence modeling that conditions on the hindsight information
including returns, goal or future trajectory. Although promising, this
supervised paradigm overlooks the core objective of RL that maximizes the
return. This overlook directly leads to the lack of trajectory stitching
capability that affects the sequence model learning from sub-optimal data. In
this work, we introduce the concept of max-return sequence modeling which
integrates the goal of maximizing returns into existing sequence models. We
propose Reinforced Transformer (Reinformer), indicating the sequence model is
reinforced by the RL objective. Reinformer additionally incorporates the
objective of maximizing returns in the training phase, aiming to predict the
maximum future return within the distribution. During inference, this
in-distribution maximum return will guide the selection of optimal actions.
Empirically, Reinformer is competitive with classical RL methods on the D4RL
benchmark and outperforms state-of-the-art sequence model particularly in
trajectory stitching ability. Code is public at
https://github.com/Dragon-Zhuang/Reinformer.

该论文介绍了最大回报的序列建模概念，提出了 Reinforced Transformer（Reinformer）作为一个整合了最大化回报目标的序列模型。Reinformer 在训练阶段将最大化回报的目标纳入模型，并在推断时引导最优动作的选择，从而在轨迹拼接能力上优于传统强化学习方法和现有的序列模型。