Research on a Dynamic Algorithm of Simulated Warehouse Environment Based on Q-learning

This paper uses Q-learning and warehouse simulation environment path planning algorithm training and research. The application environment is a dynamic and complex environment for a real warehouse. However, traditional research is more focused on exploring the Q-learning algorithm itself, focusing on the algorithm itself rather than the application, which lacks practicability. Therefore, this study will take the real map of the warehouse as a sample can combine Q-learning to simulate the algorithm closer to reality. The algorithm simulation will be carried out in a unique three-stage form to fit the influence of dynamic and changeable on the algorithm training in the actual situation. After algorithm training and simulation, the visual path result of Q-learning in the warehouse map is obtained. Based on the result, the advantages and disadvantages of Q-learning in warehouse path planning are discussed, and suggestions for future algorithm improvement. Q-learning algorithm is a promising algorithm. Through its research, more mature path planning results can be obtained, and it can be used to optimize cargo transportation efficiency and warehouse management mode in industrial production, logistics, and other fields, to obtain actual benefits.