UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
arXiv:2606.19328v1 Announce Type: cross Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design. However, existing methods typically rely on passive data collection and suffer from poor sample efficiency, especially during the early stages of learning. We introduce a model-based approach that actively directs exploration by jointly reasoning over uncertainties in the reward, dynamics, and value functions. Our method, Uncertainty-Balanced Preference Planning (UBP2), uses ensembles of rewar
The continuous drive for more efficient and autonomous AI systems necessitates breakthroughs in fundamental reinforcement learning, especially as complex environments become more common for AI deployment.
Improved sample efficiency in preference-based reinforcement learning can significantly accelerate the development and deployment of AI agents in real-world scenarios where explicit reward design is difficult or impossible.
The ability of AI systems to learn complex behaviors from limited human feedback is enhanced, moving towards more robust and generalizable agentic capabilities.
- · AI agents developers
- · Robotics companies
- · Automation sector
- · AI research institutions
- · Companies reliant on extensive manual reward engineering
- · AI platforms with inefficient learning mechanisms
More sophisticated and human-aligned AI agents can be developed with less data and overhead.
This could accelerate the adoption of AI agents in various industries, leading to new autonomous applications and services.
The reduced need for human supervision in reward design might contribute to faster AI development cycles and new forms of human-AI collaboration.
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Read at arXiv cs.AI