Abstract
The rectangular arrangement problem is widely used, which refers to the optimization problem of putting a finite number of small rectangles of different sizes into a large rectangle without overlapping to maximize its utilization. It belongs to the optimization problem of NP complete class. However, when the number of samples increases the difficulty and time of scheduling increases significantly. In order to solve the problem of large and medium-scale rectangular packing, deep Q-learning based on priority experience replay is used to improve the utilization rate of the board and reduce the calculation time of packing, so that the agent can effectively improve board utilization and increase efficiency in large- and medium scale two-dimensional rectangular packing.
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