LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
Transfer learning in reinforcement learning (RL) seeks to accelerate learning in new tasks by leveraging knowledge from related sources. Existing neurosymbolic transfer methods, however, typically rely on manually specified task automata, assume a single source task, and use fixed knowledge-integration mechanisms that cannot adapt to varying source relevance. We propose LANTERN, a unified framework for multi-source neurosymbolic transfer that addresses these limitations through three components: (i) deterministic finite automata generated from natural language task descriptions using large language models, (ii) semantic embedding-based aggregation of multiple source policies weighted by c...
| Authors | Mahyar Alinejad, Yue Wang, Amrit Singh Bedi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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