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A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

Paper proposing a taxonomy for distributional shifts in reinforcement learning.

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A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

By Ardianto Wibowo, Paulo E Santos, Amer Baghdadi, Matthew Stephenson, Karl Sammut, Jean-Philippe DiguetarXiv
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The paper develops a unified causal-origin taxonomy to characterize sources of distributional shift in RL. It relates ID/OOD generalization to non-stationary settings and introduces an evaluation framework for measuring shift impact and adaptation.

The proposed taxonomy grounds distributional shift in the causal-origin structure of RL, supporting systematic analysis of robustness under distributional shift.

Abstract

The paper develops a unified causal-origin taxonomy to characterize sources of distributional shift in RL. It relates ID/OOD generalization to non-stationary settings and introduces an evaluation framework for measuring shift impact and adaptation. The proposed taxonomy grounds distributional shift in the causal-origin structure of RL, supporting systematic analysis of robustness under distributional shift.

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distributional shiftsreinforcement learningcausal origin taxonomygeneralizationnon-stationary settingsAI AgentsAgent MemoryLarge Language ModelsSemantic Interoperability
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