Neuro-Symbolic Meta-Policies Improve Temporal Knowledge-Graph Memory in Reinforcement Learning
A new preprint presents a neuro-symbolic meta-policy for reinforcement learning under partial observability, utilizing temporal knowledge-graph memory in the RoomKG environment. The method represents hidden states and observations as RDF graphs with temporal annotations, enabling adaptive and inspectable memory management. The qualifier-aware StarE-GNN configuration achieves superior held-out performance compared to other symbolic, neural, and neuro-symbolic systems, while maintaining traceability of memory decisions.
Why it matters: This work advances both performance and transparency in memory management for reinforcement learning agents by integrating semantic web technologies with neuro-symbolic methods.
Full story at: arXiv AI/ML ↗