AI RESEARCH
Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
arXiv CS.LG
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ArXi:2605.20272v1 Announce Type: new While humans readily generalize abstract concepts to 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 Marko 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.