[4Rin1] Interactive 2

[4Rin1-84] Online optimization of AGV transport system using deep reinforcement learning

〇Jaebok Sung¹, Kei Takahashi¹, Dinesh Malla^1,2, Katsuyoshi Sakamoto¹, koichi Yamaguchi¹, Tomah Sogabe^1,2 (1.UEC, 2.Grid Inc.)

Keywords:Deep reinforcement learning, AGV transportation system, Online optimization

Recently, many of the manufacturing systems adopt AGV(Automated Guided Vehicle) to respond to diversifying needs in the manufacturing industry. However, it is difficult to solve the optimization problem of the AGV transport system by using mathematical optimization. In this study, we use Deep Q Network, one of the method of deep reinforcement learning, to optimize AGV transport system. After train the neural network that decide the behavior of the AGVs by the simulation of practice model, evaluate it by comparing with the result of rule-based simulation and applying trained neural network to the testing model.