<ul><li>The study focuses on the charging scheduling problem for Electric Buses (EBs) using Deep Reinforcement Learning (DRL).</li><li>Hierarchical DRL (HDRL) is proposed to tackle the challenge of long-range multi-phase planning with sparse rewards.</li><li>The algorithm involves Hierarchical Double Deep Q-Network (HDDQN)-Hindsight Experience Replay (HER) for decision-making at different temporal resolutions.</li><li>Numerical experiments with real-world data have been conducted to assess the algorithm's performance.</li></ul>

Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning

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