Efficient container relocation, or reshuffling, is important for terminal yard management, especially with the increasing global volume of containerized trade. To solve the container relocation problem in intermodal terminals, this work focuses on a simple yard structure and aims to investigate the application of reinforcement learning, especially the Q-learning method. The results, such as the relocation rate, will be assessed using a heuristic approach.

* Previous knowledge of Python and reinforcement learning methods is mandatory.

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