[2603.01488] LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning

Computer Science > Artificial Intelligence arXiv:2603.01488 (cs) [Submitted on 2 Mar 2026] Title:LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning Authors:Chang Yao, Jinghui Qin, Kebing Jin, Hankz Hankui Zhuo View a PDF of the paper titled LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning, by Chang Yao and 3 other authors View PDF HTML (experimental) Abstract:Despite achieving remarkable success in complex tasks, Deep Reinforcement Learning (DRL) is still suffering from critical issues in practical applications, such as low data efficiency, lack of interpretability, and limited cross-environment transferability. However, the learned policy generating actions based on states are sensitive to the environmental changes, struggling to guarantee behavioral safety and compliance. Recent research shows that integrating Large Language Models (LLMs) with symbolic planning is promising in addressing these challenges. Inspired by this, we introduce a novel LLM-driven closed-loop framework, which enables semantic-driven skill reuse and real-time constraint monitoring by mapping natural language instructions into executable rules and semantically annotating automatically created options. The proposed approach utilizes the general knowledge of LLMs to facilitate exploration efficiency and adapt to transferable options for similar environments, and provides inherent interpretability through semantic ann...