Prompting Decision Transformer for Few-Shot Policy Generalization

• URL: http://arxiv.org/abs/2206.13499v1
• Date: Mon, 27 Jun 2022 17:59:17 GMT
• Title: Prompting Decision Transformer for Few-Shot Policy Generalization
• Authors: Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan
• Abstract summary: We propose a Prompt-based Decision Transformer (Prompt-DT) to achieve few-shot adaptation in offline RL. Prompt-DT is a strong few-shot learner without any extra finetuning on unseen target tasks.
• Score: 98.0914217850999
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptation through better algorithm design, we investigate the effect of architecture inductive bias on the few-shot learning capability. We propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the sequential modeling ability of the Transformer architecture and the prompt framework to achieve few-shot adaptation in offline RL. We design the trajectory prompt, which contains segments of the few-shot demonstrations, and encodes task-specific information to guide policy generation. Our experiments in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot learner without any extra finetuning on unseen target tasks. Prompt-DT outperforms its variants and strong meta offline RL baselines by a large margin with a trajectory prompt containing only a few timesteps. Prompt-DT is also robust to prompt length changes and can generalize to out-of-distribution (OOD) environments.

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