AI RESEARCH
Leveraging Human Feedback for Semantically-Relevant Skill Discovery
arXiv CS.LG
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ArXi:2604.24127v1 Announce Type: new Unsupervised skill discovery in reinforcement learning aims to intrinsically motivate agents to discover diverse and useful behaviours. However, unconstrained approaches can produce unsafe, unethical, or misaligned behaviours. To mitigate these risks and improve the practical desireability of discovered skills, recent work grounds the discovery process by leveraging human preference feedback.