Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
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 ...
| Authors | Taewoon Kim, Vincent François-Lavet, Michael Cochez |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 3 October 2026 |
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