Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
A new theoretical model has been proposed to enhance Out-of-Distribution (OOD) generalization in Reinforcement Learning (RL) agents. The model focuses on using smaller abstract state spaces to improve performance in complex tasks. This research aims to motivate further exploration into RL architectures that can adapt across varying levels of task complexity.
- ▪The study presents a model for achieving OOD generalization in RL agents.
- ▪It extends existing frameworks to Partially Observable Markov Decision Processes (POMDPs).
- ▪The research shows that reducing the size of an agent's abstract state space can enhance test performance.
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Computer Science > Machine Learning arXiv:2605.20272 (cs) [Submitted on 19 May 2026] Title:Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning Authors:Nasehatul Mustakim, Lucas Lehnert View a PDF of the paper titled Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning, by Nasehatul Mustakim and Lucas Lehnert View PDF 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.
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