Active teacher selection for reward learning
📰 ArXiv cs.AI
Learn how to implement active teacher selection for reward learning using the Hidden Utility Bandit framework to improve machine learning systems
Action Steps
- Formalize the problem of learning from multiple teachers using the Hidden Utility Bandit framework
- Model differences in teacher rationality, expertise, and costliness
- Implement active teacher selection to optimize reward learning
- Evaluate the performance of the HUB framework using simulated experiments
- Apply the HUB framework to real-world reward learning tasks
Who Needs to Know This
Machine learning engineers and researchers can benefit from this technique to improve the efficiency of reward learning systems, especially when dealing with heterogeneous populations of human teachers
Key Insight
💡 The HUB framework can effectively model and select the most informative teachers to improve the efficiency of reward learning systems
Share This
Improve reward learning with active teacher selection using Hidden Utility Bandit framework #machinelearning #rewardlearning
Key Takeaways
Learn how to implement active teacher selection for reward learning using the Hidden Utility Bandit framework to improve machine learning systems
Full Article
Title: Active teacher selection for reward learning
Abstract:
arXiv:2310.15288v3 Announce Type: replace Abstract: Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human teacher, despite gathering feedback from large and heterogeneous populations. We propose the Hidden Utility Bandit (HUB) framework to model differences in teacher rationality, expertise, and costliness, formalizing the problem of learning from multi
Abstract:
arXiv:2310.15288v3 Announce Type: replace Abstract: Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human teacher, despite gathering feedback from large and heterogeneous populations. We propose the Hidden Utility Bandit (HUB) framework to model differences in teacher rationality, expertise, and costliness, formalizing the problem of learning from multi
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