Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning

The paper introduces a neuro‑symbolic architecture that combines symbolic planning, reinforcement learning, and a large language model (LLM) to address novelties in dynamic open‑world environments. The LLM is used to identify missing operators, generate symbolic plans, and write reward functions, enabling the reinforcement learning agent to learn control policies for newly identified operators. The proposed method outperforms state‑of‑the‑art approaches in both operator discovery and learning within continuous robotic domains.

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• llms
• agents
• reinforcement-learning
• robotics
• benchmarks