用大模型自动生成任务规则，多源知识动态加权，提升强化学习迁移效率。
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
- 基于自然语言生成任务自动机，替代人工设计规则。
- 多源策略按语义相似度加权融合，提升样本效率40%-60%。
- 根据误差与不确定性自适应调节知识传递，对不匹配源鲁棒。
强化学习中的迁移学习旨在通过复用相关任务的知识来加速新任务的学习。现有神经符号迁移方法通常依赖手动定义的任务自动机，仅支持单源迁移，且使用固定的知识融合机制，难以适应不同来源的相关性变化。本文提出LANTERN，一个统一的多源神经符号迁移框架，包含三个核心组件：(i) 利用大语言模型从自然语言任务描述中生成确定性有限自动机；(ii) 基于语义嵌入的多源策略聚合，权重由跨任务相似度决定；(iii) 基于时序差分误差和语义不确定性的自适应师生门控机制。在资源管理、导航和控制等多个领域，LANTERN相比现有基线实现40%-60%的样本效率提升，同时对对齐不佳的源仍保持鲁棒性。结果表明，多源、自适应加权的神经符号迁移可显著提升符号化强化学习场景下的可扩展性与鲁棒性。
原文摘要 · Abstract (English)
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 cross-task similarity, and (iii) adaptive teacher-student gating based on temporal-difference error and semantic uncertainty. Across domains spanning resource management, navigation, and control, LANTERN achieves 40-60% improvements in sample efficiency over existing baselines while remaining robust to poorly aligned sources. These results demonstrate that multi-source, adaptively weighted neurosymbolic transfer can improve scalability and robustness in symbolic RL settings.
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