西南交通大学交通运输与物流学院
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卢锦澎, 梁宏斌. 基于深度Q网络的机器人路径规划研究综述[J]. 传感器与微系统, 2024,(6):1-5.
卢锦澎, 梁宏斌. 基于深度Q网络的机器人路径规划研究综述[J]. 传感器与微系统, 2024,(6):1-5. DOI: 10.13873/J.1000-9787(2024)06-0001-05.
随着深度强化学习的不断发展,深度Q网络(DQN)在机器人路径规划中得到广泛关注和研究。首先,简要介绍DQN以及Nature DQN、Double DQN、Dueling DQN和D3QN等算法的基本原理和改进思想。针对算法存在的样本获取成本高和交互效率低的问题,系统梳理并总结了从奖励函数、探索能力、样本利用率等方面进行优化的研究成果和思路。最后,讨论了DQN在现代物流中进行机器人路径规划的优势,对每个场景提出了算法的优化方向,涵盖状态空间、动作空间以及奖励函数等多个关键方面。
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肖浩,廖祝华,刘毅志,刘思林,刘建勋.实际环境中基于深度Q学习的无人车路径规划[J].山东大学学报(工学版),2021(01).
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