Leveraging Human Feedback for Semantically-Relevant Skill Discovery
📰 ArXiv cs.AI
Learn how to leverage human feedback for semantically-relevant skill discovery in reinforcement learning to improve agent behavior
Action Steps
- Collect human feedback data on agent behaviors
- Implement a preference-based approach to skill discovery
- Use semantic relevance to filter and rank discovered skills
- Evaluate the safety and alignment of discovered skills
- Refine the skill discovery process based on human feedback and evaluation results
Who Needs to Know This
Researchers and engineers working on reinforcement learning and AI safety can benefit from this approach to improve the practical desirability of discovered skills
Key Insight
💡 Human feedback can be used to ground skill discovery in reinforcement learning and improve the practical desirability of discovered skills
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🤖 Improve RL agent behavior with human feedback! 📈
Key Takeaways
Learn how to leverage human feedback for semantically-relevant skill discovery in reinforcement learning to improve agent behavior
Full Article
Title: Leveraging Human Feedback for Semantically-Relevant Skill Discovery
Abstract:
arXiv:2604.24127v1 Announce Type: cross Abstract: 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. However, preference-based approaches are feedback-inef
Abstract:
arXiv:2604.24127v1 Announce Type: cross Abstract: 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. However, preference-based approaches are feedback-inef
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