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A deep reinforcement learning approach for integrated optimization of train scheduling and rolling stock circulation planning
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DOI：10.1016/j.cie.2025.111784.png)
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Abstract
En 中文
• Proposes a deep reinforcement learning framework for metro train timetable and rolling stock circulation planning. • Develops a hybrid action space environment to coordinate discrete and continuous operational decisions. • Designs a potential-based reward shaping mechanism to enhance learning efficiency. • Handles operational constraints via action masking and action embedding mechanisms.
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6.5
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1.0W
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3.8W
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