Automated Rewards via LLM-Generated Progress Functions Article Swipe

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Vishnu Sarukkai , Brennan Shacklett , Zander Majercik , Kush Bhatia , Christopher Ré , Kayvon Fatahalian ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.09187

Large Language Models (LLMs) have the potential to automate reward engineering by leveraging their broad domain knowledge across various tasks. However, they often need many iterations of trial-and-error to generate effective reward functions. This process is costly because evaluating every sampled reward function requires completing the full policy optimization process for each function. In this paper, we introduce an LLM-driven reward generation framework that is able to produce state-of-the-art policies on the challenging Bi-DexHands benchmark with 20x fewer reward function samples than the prior state-of-the-art work. Our key insight is that we reduce the problem of generating task-specific rewards to the problem of coarsely estimating task progress. Our two-step solution leverages the task domain knowledge and the code synthesis abilities of LLMs to author progress functions that estimate task progress from a given state. Then, we use this notion of progress to discretize states, and generate count-based intrinsic rewards using the low-dimensional state space. We show that the combination of LLM-generated progress functions and count-based intrinsic rewards is essential for our performance gains, while alternatives such as generic hash-based counts or using progress directly as a reward function fall short.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.09187
PDF: https://arxiv.org/pdf/2410.09187
OA Status: green
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2410.09187

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Title: Automated Rewards via LLM-Generated Progress Functions

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-11

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Authors: Vishnu Sarukkai, Brennan Shacklett, Zander Majercik, Kush Bhatia, Christopher Ré, Kayvon Fatahalian

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Landing page: https://arxiv.org/abs/2410.09187

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