The railway industry is crucial for a country’s infrastructure, enabling the efficient transport of products over long distances at a lower cost. One of the main objectives of modern railway engineering is developing automatic train control systems to enhance operational efficiency and safety, save energy, and reduce costs. This paper proposes a planner capable of creating driving models for heavy haul trains on a real railway section, respecting speed limits without compromising travel time and fuel consumption. The planner was trained using a reinforcement learning algorithm called Deep Q-Network (DQN) and an analytical train driving simulator also developed in this work, which models the dynamic of a railway composition. Results show acceptable travel times but there is room for improvement in respecting speed limits, specially when it is close to the end of the journey. Fuel consumption slightly increased compared to current standards, indicating the need to improve the DQN algorithm’s reward function.