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This paper proposes an LLM-assisted semantic stop embedding approach for reinforcement-learning-based bus holding control, reducing headway variability, bunching events, and passenger waiting time in simulations.
提出了一种不确定性感知的地质导向框架，该框架将用于概率地下解释的粒子滤波与用于序贯决策的强化学习相结合，并在工业模拟器上进行了评估。