Reducing unproductive walking times is an important planning problem in picker-to-parts warehouses. In such facilities, the worker traverses the warehouse to collect all items of an order. The so-called picker routing problem aims at a shortest tour through the warehouse, which allows the picker to visit all pick positions before returning to the central depot.

The aim of the thesis is to develop a reinforcement learning approach that optimizes the picker routing problem in a small-sized warehouse. The procedure can, but does not have to, be adapted from the famous taxi environment.

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