检索结果-南通市图书馆

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

Chinese Journal of Aeronautics 2023年第5期36卷 377-391页

作者： Zijian HU Xiaoguang GAO Kaifang WAN neretin evgeny Jinliang LI School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China School of Robotic and Intelligent Systems Moscow Aviation Institute(National Research University)Moscow 125993Russia Electromagnetic Space Operations and Applications Laboratory The 29th Research Institute of China Electronics Technology Group CorporationChengdu 610036China

As an advanced combat weapon,Unmanned Aerial Vehicles(UAVs)have been widely used in military *** this paper,we formulated the Autonomous Navigation Control(ANC)problem of UAVs as a Markov Decision Process(MDP)and proposed a novel Deep Reinforcement Learning(DRL)method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown *** solve the problem of limited training experience,the proposed Imaginary Filtered Hindsight Experience Replay(IFHER)generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the *** welldesigned goal,episode,and quality filtering strategies ensure that only high-quality augmented experiences can be stored,while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high *** training in a complex environment constructed based on the parameters of a real UAV,the proposed IFHER algorithm improves the convergence speed by 28.99%and the convergence result by 11.57%compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient(TD3)*** testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER ***,the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.

关键词： Artificial intelligence Autonomous navigation control Deep reinforcement learning Hindsight experience replay UAV

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Manoeuvre decision-making of unmanned aerial vehicles in air combat based on an expert actor-based soft actor critic algorithm

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1608-1619页

作者： Bo Li Shuangxia Bai Shiyang Liang Rui Ma evgeny neretin Jingyi Huang School of Electronics and Information Northwestern Polytechnical UniversityXi'anChina General department Avic Luoyang Electro-optical Equipment Research InstituteLuoyangChina General department Xi'an Electronic Engineering Research InstituteXi'anChina School of Robotic and Intelligent Systems Moscow Aviation InstituteMoscowRussia

The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more *** aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy;however,limits their combat capabilities,which proves to be very *** meet the challenge,this article proposes an autonomous manoeuvre decision model using an expert actor-based soft actor critic algorithm that reconstructs empirical replay buffer with expert ***,the algorithm uses a small amount of expert experience to increase the diversity of the samples,which can largely improve the exploration and utilisation efficiency of deep reinforcement *** to simulate the complex battlefield environment,a one-toone air combat model is established and the concept of missile's attack region is *** model enables the one-to-one air combat to be simulated under different initial battlefield *** results show that the expert actor-based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster,and converge more quickly,compared with the soft actor critic algorithm.

关键词： artificial intelligence techniques autonomous drone deep neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Autonomous air combat decision-making of UAV based on parallel self-play reinforcement learning

引用

CAAI Transactions on Intelligence Technology 2023年第1期8卷 64-81页

作者： Bo Li Jingyi Huang Shuangxia Bai Zhigang Gan Shiyang Liang neretin evgeny Shouwen Yao School of Electronics Information Northwestern Polytechnical UniversityXi'anChina Avic Luoyang Electro-optical Equipment Research Institute LuoyangChina Moscow Aviation Institute 4 Volokolamskoe HighwayMoscowRussia School of Mechanical Engineering Beijing Institute of TechnologyBeijingChina

Aiming at addressing the problem of manoeuvring decision-making in UAV air combat,this study establishes a one-to-one air combat model,defines missile attack areas,and uses the non-deterministic policy Soft-Actor-Critic(SAC)algorithm in deep reinforcement learning to construct a decision model to realize the manoeuvring *** the same time,the complexity of the proposed algorithm is calculated,and the stability of the closed-loop system of air combat decision-making controlled by neural network is analysed by the Lyapunov *** study defines the UAV air combat process as a gaming process and proposes a Parallel Self-Play training SAC algorithm(PSP-SAC)to improve the generalisation performance of UAV control *** results have shown that the proposed algorithm can realize sample sharing and policy sharing in multiple combat environments and can significantly improve the generalisation ability of the model compared to independent training.

关键词： air combat decision deep reinforcement learning parallel self-play SAC algorithm UAV

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

UAV Maneuvering Decision-Making Algorithm Based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

引用

Journal of Artificial Intelligence and Technology 2022年第1期2卷 16-22页

作者： Bai Shuangxia Song Shaomei Liang Shiyang Wang Jianmei Li Bo neretin evgeny School of Electronics and Information Northwestern Polytechnical UniversityXi’anChina Beijing Electro-Mechanical Engineering Institute BeijingChina Avic Luoyang Electro-optical Equipment Research Institute LuoyangChina School of Robotic and Intelligent Systems Moscow Aviation InstituteMoscowRussia

Aiming at intelligent decision-making of unmanned aerial vehicle(UAV)based on situation information in air combat,a novelmaneuvering decision method based on deep reinforcement learning is proposed in this *** autonomous maneuvering model ofUAV is established byMarkovDecision *** Twin DelayedDeep Deterministic Policy Gradient(TD3)algorithm and the Deep Deterministic Policy Gradient(DDPG)algorithm in deep reinforcement learning are used to train the model,and the experimental results of the two algorithms are analyzed and *** simulation experiment results show that compared with the DDPG algorithm,the TD3 algorithm has stronger decision-making performance and faster convergence speed and is more suitable for solving combat *** algorithm proposed in this paper enables UAVs to autonomously make maneuvering decisions based on situation information such as position,speed,and relative azimuth,adjust their actions to approach,and successfully strike the enemy,providing a new method for UAVs to make intelligent maneuvering decisions during air combat.

关键词： air combat DDPG maneuvering decision-making TD3

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

异策略深度强化学习中的经验回放研究综述

引用

自动化学报 2023年第11期49卷 2237-2256页

作者：胡子剑高晓光万开方张乐天汪强龙 neretin evgeny 西北工业大学电子信息学院西安710129 西安电子科技大学外国语学院西安710126 莫斯科航空学院机器人与智能系统学院俄罗斯莫斯科125993

作为一种不需要事先获得训练数据的机器学习方法,强化学习(Reinforcement learning,RL)在智能体与环境的不断交互过程中寻找最优策略,是解决序贯决策问题的一种重要方法.通过与深度学习(Deep learning,DL)结合,深度强化学习(Deep reinfo... 详细信息

作为一种不需要事先获得训练数据的机器学习方法,强化学习(Reinforcement learning,RL)在智能体与环境的不断交互过程中寻找最优策略,是解决序贯决策问题的一种重要方法.通过与深度学习(Deep learning,DL)结合,深度强化学习(Deep reinforcement learning,DRL)同时具备了强大的感知和决策能力,被广泛应用于多个领域来解决复杂的决策问题.异策略强化学习通过将交互经验进行存储和回放,将探索和利用分离开来,更易寻找到全局最优解.如何对经验进行合理高效的利用是提升异策略强化学习方法效率的关键.首先对强化学习的基本理论进行介绍;随后对同策略和异策略强化学习算法进行简要介绍;接着介绍经验回放(Experience replay,ER)问题的两种主流解决方案,包括经验利用和经验增广;最后对相关的研究工作进行总结和展望.

关键词：深度强化学习异策略经验回放人工智能

来源：

维普期刊数据库

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：