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This research investigates the application of Deep Reinforcement Learning (DRL) to optimize train dispatching and increase section throughput in railway operations. The study demonstrates that a DRL agent can enhance section throughput by 21.7% to 24.3% compared to traditional heuristic methods while maintaining safety and reliability. The findings highlight the potential of AI-driven solutions to address ongoing challenges in train scheduling and traffic control within complex railway networks.
- Author
- triloklimendra123
- Language
- EN