GRAIL Framework Enables Autonomous Relational Concept Grounding in Neuro-Symbolic Reinforcement Learning
A novel framework known as GRAIL (Grounding Relational Agents through Interactive Learning) has been introduced to autonomously establish relational concepts within neuro-symbolic reinforcement learning (NeSy-RL). This innovative method removes the necessity for human experts to manually specify concepts such as "left of" or "close by," which has historically restricted adaptability due to varying semantics across different contexts. GRAIL utilizes large language models (LLMs) to offer generic representations of concepts as weak supervision, subsequently refining these through interactions within the environment to capture specific semantics. It effectively tackles issues related to sparse reward signals and concept misalignment common in underdetermined settings. Validation experiments were performed on Atari games, and the research was published on arXiv under identifier 2604.16871v1, marking a significant advancement in AI research.
Key facts
- GRAIL (Grounding Relational Agents through Interactive Learning) is a new framework for neuro-symbolic reinforcement learning
- It autonomously grounds relational concepts through environmental interaction
- Eliminates need for human experts to manually define relational concepts
- Uses large language models (LLMs) for generic concept representations as weak supervision
- Refines concept representations to capture environment-specific semantics
- Addresses sparse reward signals and concept misalignment in underdetermined environments
- Experiments conducted on Atari games
- Research announced on arXiv with identifier 2604.16871v1
Entities
Institutions
- arXiv