arXiv:2606.06123v1 Announce Type: new Abstract: When learning to walk, infants seem to address a coarse version of the problem first - stay upright, reach the caregiver - and refine it only when further practice at that resolution stops paying off. Reinforcement learning offers multiple techniques for building simple versions of complex tasks, but lacks general principles for how to dynamically adjust the granularity of these abstractions during learning. This paper proposes one such principle: refine the abstraction as soon as the learning error within it becomes comparable to the error induc
The continuous drive for more efficient and adaptable AI systems, particularly in reinforcement learning, makes research into dynamic abstraction essential for scaling capabilities.
This research provides a fundamental principle for dynamically adjusting the complexity of tasks for AI, potentially leading to more robust and generalized learning agents.
AI systems could potentially learn complex tasks more efficiently and adaptively, moving beyond fixed abstractions to self-adjust their learning granularity.
- · AI researchers
- · Reinforcement learning developers
- · Robotics industry
- · AI applications reliant on manually tuned abstractions
Improved performance and sample efficiency in complex reinforcement learning tasks, such as robotics control.
Accelerated development of more capable and self-sufficient AI agents in diverse environments.
Enhanced ability for AI to learn and adapt in unpredictable real-world scenarios, reducing the need for extensive pre-programming.
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Read at arXiv cs.LG