Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (3): 418-425.doi: 10.11959/j.issn.2096-6652.202244
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Zhou YU1, Jing BI1, Haitao YUAN2
Revised:
2022-08-15
Online:
2022-09-15
Published:
2022-09-01
Supported by:
CLC Number:
Zhou YU,Jing BI,Haitao YUAN. A path planning method for complex naval battle field based on an improved DQN algorithm[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 418-425.
[1] | 史进, 董瑶, 白振东 ,等. 移动机器人动态路径规划方法的研究与实现[J]. 计算机应用, 2017,37(11): 3119-3123. |
SHI J , DONG Y , BAI Z D ,et al. Research and implementation of mobile robot path planning method[J]. Journal of Computer Applications, 2017,37(11): 3119-3123. | |
[2] | 魏彤, 龙琛 . 基于改进遗传算法的移动机器人路径规划[J]. 北京航空航天大学学报, 2020,46(4): 703-711. |
WEI T , LONG C . Path planning for mobile robot based on improved genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020,46(4): 703-711. | |
[3] | YOU X M , LIU S , LYU J Q . Ant colony algorithm based on dynamic search strategy and its application on path planning of robot[J]. Control and Decision, 2017,32(3): 552-556. |
[4] | 默凡凡 . 基于 Q 学习算法的移动机器人路径规划方法研究[D]. 北京:北京工业大学, 2016. |
MO F F . Research on path planning of mobile robot based on Q learning algorithm[D]. Beijing:Beijing University of Technology, 2016. | |
[5] | 姜涛, 王建中, 施家栋 . 小型移动机器人自主返航路径规划方法[J]. 计算机工程, 2015,41(1): 164-168. |
JIANG T , WANG J Z , SHI J D . Path planning method in autonomous returning for mini-mobile robot[J]. Computer Engineering, 2015,41(1): 164-168. | |
[6] | 刘洁, 赵海芳, 周德廉 . 一种改进量子行为粒子群优化算法的移动机器人路径规划[J]. 计算机科学, 2017,44(S2): 123-128. |
LIU J , ZHAO H F , ZHOU D L . Improved quantum behaved particle swarm optimization algorithm for mobile robot path planning[J]. Computer Science, 2017,44(S2): 123-128. | |
[7] | 孙炜, 吕云峰, 唐宏伟 ,等. 基于一种改进A*算法的移动机器人路径规划[J]. 湖南大学学报(自然科学版), 2017,44(4): 94-101. |
SUN W , LYU Y F , TANG H W ,et al. Mobile robot path planning based on an improved A* algorithm[J]. Journal of Hunan University (Natural Sciences), 2017,44(4): 94-101. | |
[8] | 胡俊, 朱庆保 . 未知环境下基于有先验知识的滚动Q学习机器人路径规划[J]. 控制与决策, 2010,25(9): 1364-1368. |
HU J , ZHU Q B . Path planning of robot for unknown environment based on prior knowledge rolling Q-learning[J]. Control and Decision, 2010,25(9): 1364-1368. | |
[9] | XIN J , ZHAO H , LIU D ,et al. Application of deep reinforcement learning in mobile robot path planning[C]// Proceedings of 2017 Chinese Automation Congress. Piscataway:IEEE Press, 2017: 7112-7116. |
[10] | TAI L , LIU M . Towards cognitive exploration through deep reinforcement learning for mobile robots[J]. arXiv preprint,2016,arXiv:1610.01733. |
[11] | 江其洲, 曾碧 . 基于深度强化学习的移动机器人导航策略研究[J]. 计算机测量与控制, 2019,27(8): 217-221,226. |
JIANG Q Z , ZENG B . Research on navigation strategy of mobile robot based on deep reinforcement learning[J]. Computer Measurement& Control, 2019,27(8): 217-221,226. | |
[12] | 张荣霞, 武长旭, 孙同超 ,等. 深度强化学习及在路径规划中的研究进展[J]. 计算机工程与应用, 2021,57(19): 44-56. |
ZHANG R X , WU C X , SUN T C ,et al. Progress on deep reinforcement learning in path planning[J]. Computer Engineering and Applications, 2021,57(19): 44-56. | |
[13] | 刘朝阳, 穆朝絮, 孙长银 . 深度强化学习算法与应用研究现状综述[J]. 智能科学与技术学报, 2020,2(4): 314-326. |
LIU Z Y , MU C X , SUN C Y . An overview on algorithms and applications of deep reinforcement learning[J]. Chinese Journal of Intelligent Science and Technology, 2020,2(4): 314-326. | |
[14] | 韩向敏, 鲍泓, 梁军 ,等. 一种基于深度强化学习的自适应巡航控制算法[J]. 计算机工程, 2018,44(7): 32-35,41. |
HAN X M , BAO H , LIANG J ,et al. An adaptive cruise control algorithm based on deep reinforcement learning[J]. Computer Engineering, 2018,44(7): 32-35,41. | |
[15] | 刘婵, 朱永川, 白园 ,等. 基于 flocking 的多智能体群集与避障算法研究与仿真[J]. 通信技术, 2019,52(7): 1632-1638. |
LIU C , ZHU Y C , BAI Y ,et al. Flocking for multi-agent clustering and obstacle avoidance:algorithm and simulation[J]. Communications Technology, 2019,52(7): 1632-1638. | |
[16] | 刘辉, 肖克, 王京擘 . 基于多智能体强化学习的多 AGV 路径规划方法[J]. 自动化与仪表, 2020,35(2): 84-89. |
LIU H , XIAO K , WANG J B . Multi-AGV path planning method based on multi-agent reinforcement learning[J]. Automation & Instrumentation, 2020,35(2): 84-89. | |
[17] | 黄子蓉, 甯彦淞, 王莉 . 基于优先经验回放的多智能体协同算法[J]. 太原理工大学学报, 2021,52(5): 747-753. |
HUANG Z R , NING Y S , WANG L . Prioritized experience replay for multi-agent cooperation[J]. Journal of Taiyuan University of Technology, 2021,52(5): 747-753. | |
[18] | 高振洋, 秦斌 . 深度强化学习研究进展[J]. 电脑知识与技术, 2019,15(4): 157-159,173. |
GAO Z Y , QIN B . Progress in deep reinforcement learning research[J]. Computer Knowledge and Technology, 2019,15(4): 157-159,173. | |
[19] | 杨思明, 单征, 丁煜 ,等. 深度强化学习研究综述[J]. 计算机工程, 2021,47(12): 19-29. |
YANG S M , SHAN Z , DING Y ,et al. Survey of research on deep reinforcement learning[J]. Computer Engineering, 2021,47(12): 19-29. | |
[20] | 刘全, 翟建伟, 章宗长 ,等. 深度强化学习综述[J]. 计算机学报, 2018,41(1): 1-27. |
LIU Q , ZHAI J W , ZHANG Z C ,et al. A survey on deep reinforcement learning[J]. Chinese Journal of Computers, 2018,41(1): 1-27. |
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