Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving
Quick summary
arXiv:2609.27745v1 Announce Type: new Abstract: 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
Key takeaways
- arXiv:2609.27745v1 Announce Type: new Abstract: 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
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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