PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning
Simon Holk,
Daniel Marta,
Iolanda Leite
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 … Show more
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