Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos

• URL: http://arxiv.org/abs/2410.09286v1
• Date: Fri, 11 Oct 2024 22:31:39 GMT
• Title: Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos
• Authors: Harsh Mahesheka, Zhixian Xie, Zhaoran Wang, Wanxin Jin,
• Abstract summary: We introduce a language-model-assisted bi-level programming framework that enables a reinforcement learning agent to learn its reward from internet videos. The framework includes two levels: an upper level where a vision-language model (VLM) provides feedback by comparing the learner's behavior with expert videos, and a lower level where a large language model (LLM) translates this feedback into reward updates. We validate the method for reward learning from YouTube videos, and the results have shown that the proposed method enables efficient reward design from expert videos of biological agents.
• Score: 48.2044649011213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from Demonstrations, particularly from biological experts like humans and animals, often encounters significant data acquisition challenges. While recent approaches leverage internet videos for learning, they require complex, task-specific pipelines to extract and retarget motion data for the agent. In this work, we introduce a language-model-assisted bi-level programming framework that enables a reinforcement learning agent to directly learn its reward from internet videos, bypassing dedicated data preparation. The framework includes two levels: an upper level where a vision-language model (VLM) provides feedback by comparing the learner's behavior with expert videos, and a lower level where a large language model (LLM) translates this feedback into reward updates. The VLM and LLM collaborate within this bi-level framework, using a "chain rule" approach to derive a valid search direction for reward learning. We validate the method for reward learning from YouTube videos, and the results have shown that the proposed method enables efficient reward design from expert videos of biological agents for complex behavior synthesis.

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