Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability

Abstract:Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns which symbolic memory heuristic to apply at each decision point while keeping execution symbolic. Our setting uses temporal knowledge-graph memory in RoomKG, where hidden state and observations are represented as Resource Description Framework (RDF) graphs and memory is augmented with temporal RDF triple annotations. The model combines knowledge-graph encoding of memory contents with value heads for question answering, exploration, and forgetting, yielding a controller that is both adaptive and inspectable. This gives the work a direct Semantic Web grounding through RDF-based representation, annotation-compatible graph semantics, and graph-based symbolic operations over explicit memory state. On train/test room splits at long-term memory capacity of 512, the qualifier-aware StarE-GNN configuration achieves the best held-out performance among the compared symbolic, neural, and neuro-symbolic systems while preserving step-level traceability of memory-management decisions.

Artificial Intelligence (cs.AI)

arXiv:2607.18368 [cs.AI]

(or arXiv:2607.18368v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.18368 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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