LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements

• URL: http://arxiv.org/abs/2412.06877v1
• Date: Mon, 09 Dec 2024 18:43:56 GMT
• Title: LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements
• Authors: Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun, Mihaela van der Schaar,
• Abstract summary: This paper presents TEDUO, a novel training pipeline for offline language-conditioned policy learning. TEDUO operates on easy-to-obtain, unlabeled datasets and is suited for the so-called in-the-wild evaluation, wherein the agent encounters previously unseen goals and states.
• Score: 50.544186914115045
• License:
• Abstract: To develop autonomous agents capable of executing complex, multi-step decision-making tasks as specified by humans in natural language, existing reinforcement learning approaches typically require expensive labeled datasets or access to real-time experimentation. Moreover, conventional methods often face difficulties in generalizing to unseen goals and states, thereby limiting their practical applicability. This paper presents TEDUO, a novel training pipeline for offline language-conditioned policy learning. TEDUO operates on easy-to-obtain, unlabeled datasets and is suited for the so-called in-the-wild evaluation, wherein the agent encounters previously unseen goals and states. To address the challenges posed by such data and evaluation settings, our method leverages the prior knowledge and instruction-following capabilities of large language models (LLMs) to enhance the fidelity of pre-collected offline data and enable flexible generalization to new goals and states. Empirical results demonstrate that the dual role of LLMs in our framework-as data enhancers and generalizers-facilitates both effective and data-efficient learning of generalizable language-conditioned policies.

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