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Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially observable environments. We model symmetries between environmental states by partitioning the state space into equivalence classes induced by sym- metry orbits, and organise each such class as a groupoid with a designated canonical representative. This allows the agent to share what it learns across many similar environmental states simultaneously, rather than treating every orientation or position as an entirely new problem. Learning is thus carri

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