AI RESEARCH
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
arXiv CS.LG
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ArXi:2604.16871v1 Announce Type: cross 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.