Abstract
In recent years, with the advantages of intelligence, convenience and unmanned operation, Automated Guided Vehicle (AGV) have changed the previous situation of using forklifts to transport parts in automobile manufacturing. Among them, the path planning problem of AGV is one of the key problems encountered by factories when using AGV. In order to be able to choose a reasonable path in the complex factory environment and quickly transport auto parts from one production environment to the next. A production environment. However, with the expansion of the factory production line, the interference of the external environment and the requirement of real-time performance, the conventional optimization algorithm has been completely unable to meet the actual factory needs. With the wider application of deep reinforcement learning, deep reinforcement learning has gradually become an important solution to the problem of vehicle path planning. Based on a brief introduction to the conventional methods to solve the vehicle routing problem, this paper focuses on summarizing the algorithms for solving the vehicle routing problem based on reinforcement learning (RL) and deep reinforcement learning (DRL). Finally, the future research on this problem is prospected.
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Yuchun, H., Wang, C., Hua, B. (2023). A Review of Vehicle Routing Problem Based on RL and DRL. In: Wang, Y., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XII. IWAMA 2022. Lecture Notes in Electrical Engineering, vol 994. Springer, Singapore. https://doi.org/10.1007/978-981-19-9338-1_15
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DOI: https://doi.org/10.1007/978-981-19-9338-1_15
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