Revisiting the Design Choices in Max-Return Sequence Modeling

Zifeng Zhuang, Dengyun Peng, Donglin Wang, Jiacheng Liu, Xing Lei, Diyuan Shi, Ziqi Zhang

Keywords: Sequence Modeling in Offline Reinforcement Learning

Abstract: Decision Transformer (DT), free from optimal value functions fitting and policy gradient computation, attempts to solve offline reinforcement learning (RL) via supervised sequence modeling. During inference, sequence modeling requires an initial target returns assigned with expert knowledge, which blocks comprehensive evaluation on more diverse datasets. As a result, existing sequence modeling only focuses on limited evaluation on Gym datasets and some understanding is severely biased. In this paper, we aim to revisit the design choices, including architecture and context length, in sequence modeling on more diverse datasets. We utilize the max-return sequence modeling that replaces the manual target returns with maximized returns predicted by itself. We systematically investigate the impact of 1) architectural choices and 2) context lengths in max-return sequence modeling on nine datasets with varying data distributions. Abundant experiments and thorough analyses reveal that design choices are highly influenced by the dataset characteristics, which further underscores the significance of more diverse evaluation.

Primary Area: reinforcement learning

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Submission Number: 4253