GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizable policies. Relational concepts, such as "left of" or "close by", serve as foundational building blocks that structure how agents perceive and act. However, conventional approaches require human experts to manually define these concepts, limiting adaptability since concept semantics vary across environments. We propose GRAIL (Grounding Relational Agents through Interactive Learning), a framework that autonomously grounds relational concepts through environmental interaction. GRAIL leverages large language models (LLMs) to provide generic con...
| Authors | Hikaru Shindo, Henri Rößler, Quentin Delfosse et al. |
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| Primary category | cs.AI |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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