LLM-Enhanced Offline Meta-Reinforcement Learning for Multi-Task V2G Regulation: A Human-AI Collaborative Framework

The rapid proliferation of electric vehicles (EVs) presents substantial challenges to effective charging and discharging regulation within distribution networks, particularly under increasingly diverse and dynamic regulatory scenarios. Conventional reinforcement learning (RL) methods, typically limited by task-specific training, struggle to generalize effectively across varying regulatory objectives and heterogeneous spatial EV distributions. To address these limitations, this study proposes a novel offline meta-reinforcement learning (Meta-RL) framework designed to enhance the adaptability of policy networks through a dedicated meta-network for task-specific identification. Specifically, we propose an innovative meta-network enhanced by large language models (LLMs), which simultaneously captures dynamic physical trajectories derived from historical operational data and encodes expert dispatch knowledge extracted from textual instructions. By integrating these physical and semantic modalities, the meta-network effectively generates task-specific embeddings, enabling the downstream policy network to rapidly adapt and formulate context-aware regulatory strategies. Comparative experiments demonstrate that the proposed offline Meta-RL framework achieves a 47.2% average reduction in operation cost compared to conventional single-task RL methods. Ablation studies further confirm that integrating physical and semantic information significantly improves task recognition accuracy and decision-making quality, reducing adaptation time by 68.8% and average operation costs by 18.4%.