AI 中文总结
针对复杂工程系统多单元协调难题，提出基于LLM的分层框架，结合感知延续性的GRPO，在多匝道交通控制和VPP能源管理中，性能优于多种对比方法。
AI 中文摘要
在复杂工程系统中，当系统交互难以建模、运行信息异质且底层动作必须满足严格约束时，协调多个相互作用单元极具挑战性。我们提出一种基于大语言模型（LLM）的分层框架，其中LLM基于异质运行上下文协调相互作用单元，而特定任务的控制器或优化器生成可执行且感知约束的动作。我们进一步引入感知延续性的GRPO，以捕捉协调决策在后续控制区间的后果。该方法并非仅通过即时结果判断决策，还会评估当前策略下系统后续的演化情况。我们在多匝道交通控制和虚拟电厂（VPP）能源管理任务中验证该框架，使用简化系统模型进行训练，采用更真实的模拟器进行评估。在两项任务中，所提方法始终优于直接的特定任务控制与优化、端到端强化学习、基于规则和强化学习的分层协调，以及仅提示的LLM协调器，证明了异质上下文推理、分层执行和感知延续性的策略学习的价值。
英文摘要
Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational information is heterogeneous, and low-level actions must satisfy strict constraints. We propose an LLM-based hierarchical framework in which the LLM coordinates interacting units based on heterogeneous operational context, while task-specific controllers or optimizers generate executable and constraint-aware actions. We further introduce Continuation-Aware GRPO to capture the consequences of coordination decisions over subsequent control intervals. Rather than judging a decision only by its immediate outcome, the method also evaluates how the system evolves afterward under the current policy. We validate the framework on multi-ramp traffic control and virtual power plant (VPP) energy management, using simplified system models for training and more realistic simulators for evaluation. Across both tasks, the proposed method consistently outperforms direct task-specific control and optimization, end-to-end reinforcement learning, rule-based and RL-based hierarchical coordination, and prompting-only LLM coordinators, demonstrating the value of heterogeneous-context reasoning, hierarchical execution, and continuation-aware policy learning.
Comments30 pages, 5 figures
