arXiv:2605.20272v1 Announce Type: new Abstract: While humans readily generalize abstract concepts to more complex or larger tasks, building Reinforcement Learning (RL) systems with this ability remains elusive. Here, we present the first theoretical model of how such Out-of-Distribution (OOD) generalization can be achieved in RL agents. Our approach considers Partially Observable Markov Decision Processes (POMDPs) and assumes that an intelligent agent uses an abstraction function to determine which experiences can be treated as equivalent and which must be distinguished. First, we extend the e
This research addresses a fundamental limitation in current Reinforcement Learning (RL) systems, coming at a time when OOD generalization is a key bottleneck for more capable AI agents.
A theoretical model for Out-of-Distribution (OOD) generalization in RL agents represents a significant step towards enabling AI systems to learn and adapt across varying task complexities, a capability crucial for autonomous systems.
This paper offers a new theoretical framework for how RL agents can achieve abstract concept generalization, potentially leading to more robust and adaptable AI systems that are less brittle outside of their training distributions.
- · AI research labs
- · Robotics companies
- · Autonomous systems developers
- · Current brittle RL systems
- · Companies relying on narrow AI applications
RL systems will become more capable of transferring learned knowledge to novel situations without extensive retraining.
This improved generalization could accelerate the development and deployment of sophisticated AI agents across various domains, including complex decision-making and control.
More generally capable AI agents could dramatically alter industries currently reliant on human cognitive generalization, impacting white-collar work and complex physical tasks over the longer term.
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