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首页> 外文期刊>Caai Transactions on Intelligence Technology >Multi-robot path planning based on a deep reinforcement learning DQN algorithm
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Multi-robot path planning based on a deep reinforcement learning DQN algorithm

机译：基于深度加强学习DQN算法的多机器人路径规划

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摘要

The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high requirements. This study uses a deep Q-network (DQN) algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, an empirical playback mechanism, and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem: slow convergence and excessive randomness. Preceding the start of the algorithmic process, prior knowledge and prior rules are used to improve the DQN algorithm. Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems. This improves the efficiency of multi-robot path planning.
机译：“货物到人们模型的商品的无人仓库调度系统主要基于处理机器人的结构，这节省了相当大的人力并提高了仓库采摘操作的效率。然而，调度系统算法的最佳性能具有高要求。本研究在深增强学习算法中使用深度Q网络（DQN）算法，其结合了Q学习算法，经验回放机制和生产性神经网络的卷技术来生成目标 q - 解决多机器人路径规划问题。深增强学习中Q学习算法的目的是解决机器人路径规划问题的两个缺点：缓慢收敛和过度随机性。在算法过程开始之前，使用先验知识和先前规则来改进DQN算法。仿真结果表明，改进的DQN算法会收敛于经典的深度加强学习算法，可以更快地了解路径规划问题的解决方案。这提高了多机器人路径规划的效率。

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