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PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning (2402.15420v1)

Published 23 Feb 2024 in cs.RO, cs.CL, and cs.LG

Abstract: Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approach the sample-efficiency challenge by expanding the information collected per query to contain both preferences and optional text prompting. To accomplish this, we leverage the zero-shot capabilities of a LLM to reason from the text provided by humans. To accommodate the additional query information, we reformulate the reward learning objectives to contain flexible highlights -- state-action pairs that contain relatively high information and are related to the features processed in a zero-shot fashion from a pretrained LLM. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications. Additionally, the collective feedback collected serves to train a robot on socially compliant trajectories in a simulated social navigation landscape. We provide video examples of the trained policies at https://sites.google.com/view/rl-predilect

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Authors (3)
  1. Simon Holk (3 papers)
  2. Daniel Marta (3 papers)
  3. Iolanda Leite (29 papers)
Citations (4)
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