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A New Reinforcement Learning for Train Marshaling with Generalization Capability

This paper proposes a new marshaling method for assembling an outgoing train. In the proposed method, each set of freight cars that have the same destination make a group, and the desirable group layout constitutes the best outgoing train. The incoming freight cars are classified into several ``sub-tracks'' searching better assignment in order to reduce the transfer distance of locomotive. Classifications and marshaling plans based on the transfer distance of a locomotive are obtained autonomously by a reinforcement learning system. Then, the number of sub-tracks utilized in the classification is determined by the learning system in order to yield generalization capability.