强化学习
计算机科学
工作量
任务(项目管理)
调度(生产过程)
自动引导车
人工智能
运动规划
路径(计算)
分布式计算
机器人
工程类
计算机网络
运营管理
操作系统
系统工程
作者
Zuozhong Yin,Jihong Liu,Dianpeng Wang
标识
DOI:10.1142/s0218001422520152
摘要
Automated guided vehicle (AGV) is an important transportation equipment, which is widely used in warehouses and factories. In the scenarios of multi-AGVs application, an efficient AGVs task assignment strategy is beneficial for transportation costs, balance of workload and increasing distribution efficiency. The traditional method usually depends on a powerful scheduling system, which solves the task assignment problem in a regular way. In this paper, we present a decentralized framework of multi-task allocation with attention (MTAA) in deep reinforcement learning, which combines with the methods of task assignment in balance and path planning in cooperation for distribution application. As for task assignment balance, we adopt DNN network to achieve task assignment equilibrium. In multi-AGVs path planning, methods of A3C are embedded in MTAA framework, which are instrumental in improving the stationarity and performance in deep reinforcement learning application. In experiments, we designed two different scenarios under different obstacles to verify the performance of MTAA-A3C and MTAA-DQN methods. The experiments show that the proposed approach has feasibility and effectiveness used in multi-AGVs application.
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