分布式神经策略梯度算法在机器人路径规划中的数据集：该数据集建立于2024年11月，利用Python仿真平台构建生成，主要面向分布式神经策略梯度算法在机器人路径规划任务中的性能分析与验证。数据集包含集中式算法与分布式算法在两类不同路径规划场景中的数值仿真结果，用于验证所提出算法在复杂路径规划环境下的收敛性能、协同决策能力以及计算效率。该数据集基于多机器人路径规划环境构建，通过对机器人状态转移、动作选择以及环境交互过程进行数值仿真，生成不同算法在训练过程中的动态演化数据。其中，目标函数性能指标由到达目标点的奖励、机器人间或与障碍物之间产生的碰撞惩罚以及路径规划过程中的时间惩罚共同构成，用于评估算法在任务完成质量与安全性方面的综合性能；同时，数据集还记录了目标函数梯度关于策略参数的范数变化情况，用于分析神经策略梯度算法的优化稳定性与收敛特性；此外，还统计了不同算法在训练与决策过程中的运行时间，用于比较集中式与分布式方法在计算复杂度和实时性方面的差异。数据集文件均为NPY格式，共12个文件，按算法类型与场景编号组织，命名规则为\"CTP_路径结构_算法类型数据类型.npy\"，其中路径结构以数字编号标识不同规划场景（11、321），算法类型分为集中式（cen）与分布式（dis）两类，数据类型包括目标函数值（obj）、策略梯度范数（gra）及运行时间（runningtime）三类，具体文件包括CTP_11_cenobj.npy、CTP_11_cengra.npy、CTP_11_cenrunningtime.npy、CTP_11_disobj.npy、CTP_11_disgra.npy、CTP_11_disrunningtime.npy、CTP_321_cenobj.npy、CTP_321_cengra.npy、CTP_321_cenrunningtime.npy、CTP_321_disobj.npy、CTP_321_disgra.npy及CTP_321_disrunningtime.npy。数据实体主要参数共3类：目标函数值（无量纲，反映任务奖励与惩罚的综合得分）、策略梯度范数（无量纲，反映策略参数更新幅度，用于衡量算法收敛稳定性）及运行时间（单位为秒，反映算法在训练与决策过程中的计算耗时），数据按算法类型（集中式/分布式）与路径场景（场景11/场景321）两个维度进行分类划分。在课题研究过程中，该数据集用于支撑分布式神经策略梯度算法的性能验证与效能评估工作，验证所提出分布式策略更新机制在多机器人协同路径规划中的有效性，并用于分析不同通信与协同结构下算法在路径优化、碰撞规避以及训练效率等方面的性能表现，从而满足复杂动态环境下机器人集群路径规划与协同控制的仿真验证需求。
", "ds_source": "本数据集为实验仿真数据，非文献获取、镜像或购买数据。具体说明如下：
\n数据来源于课题组基于Python仿真平台自主构建的多机器人路径规划仿真环境（开源代码见https://github.com/Pengcheng-Dai/DACA），通过对集中式算法与分布式神经策略梯度算法进行数值仿真训练与测试生成。
\n仪器设备：联网计算机，Python仿真环境（NumPy用于数值计算、策略更新与梯度计算，Matplotlib用于绘图与结果可视化）。
\n方法与过程：在两类不同的多机器人路径规划场景下，对机器人状态转移、动作选择及环境交互过程进行数值仿真，分别运行集中式与分布式神经策略梯度算法，记录训练过程中的目标函数性能指标（到达目标奖励、碰撞惩罚、时间惩罚）、策略梯度范数变化及算法运行时间，自动生成相应仿真数据。
\n数据采集时间与地点：2024年11月，于实验室计算平台完成。
", "ds_process_way": "本数据集为基于强化学习算法模型仿真运算生成的数值型数据，具体加工方法如下：
\n仿真环境构建：基于Python平台搭建多机器人路径规划仿真环境，对机器人状态空间、动作空间、奖励函数及障碍物分布进行建模。
\n算法模型：采用集中式策略梯度算法与分布式神经策略梯度算法对机器人路径规划策略进行训练。其中，策略网络以神经网络形式表示，通过策略梯度法对策略参数进行迭代更新，分布式算法在此基础上引入多智能体间的局部信息交换与协同更新机制。具体模拟环境结构详见课题组开源项目https://github.com/Pengcheng-Dai/DACA。
\n数学运算与指标计算：目标函数性能指标由到达目标点奖励、碰撞惩罚（机器人间或与障碍物碰撞）以及时间惩罚三部分加权求和计算得到，用于反映每一回合（episode）的综合性能；
\n策略梯度范数数据通过对目标函数关于策略参数求梯度，并计算该梯度向量的欧几里得范数（L2范数）得到，用于衡量优化过程的收敛趋势；
\n运行时间数据通过对算法每次迭代（或每个回合）的训练与决策计算过程进行计时统计获得。
\n数据记录与处理：利用NumPy对仿真过程中产生的各项指标进行数值计算与统计汇总，并按算法类型（集中式/分布式）与场景类型分类存储为数值数组，最终利用Matplotlib绘制收敛曲线、性能对比曲线及运行时间统计图。
", "ds_quality": "本数据集中各项数值指标（目标函数性能值、策略梯度范数、运行时间等）均以双精度浮点数（float64）形式存储，由Python及NumPy数值计算环境直接输出，保留小数点后多位有效数字，未经人为截断或舍入处理，能够真实反映仿真过程中的数值变化情况。
", "ds_acq_start_time": "2024-01-01 00:00:00", "ds_acq_end_time": null, "ds_acq_place": "东南大学", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 445536, "ds_files_count": 0, "ds_format": "*npy", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7ab332cb-7d40-4d9c-afa9-f5cfce8216dd.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据格式与读取\r\n\r\n本数据集以数值数组形式存储（如.npy格式），可通过Python的NumPy库直接读取，例如使用numpy.load()或numpy.loadtxt()等函数。读取后的数据为多维数组，按算法类型（集中式/分布式）与场景类型分别组织。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "王和", "ds_serv_phone": "", "ds_serv_mail": "wanghe91@seu.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:57:49", "last_updated": "2026-07-09 10:57:49", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "", "ds_format": "*npy", "ds_source": "This dataset consists of simulation-generated data and was not obtained from literature, mirroring, purchase, or external download. Details are as follows:\r\nThe data was generated by the research group based on a self-developed multi-robot path planning simulation environment built on the Python simulation platform (open-source code available at https://github.com/Pengcheng-Dai/DACA), through numerical simulation training and testing of centralized algorithms and distributed neural policy gradient algorithms.\r\nEquipment: A networked computer running a Python simulation environment (NumPy for numerical computation, policy updates, and gradient calculations; Matplotlib for plotting and visualization of results).\r\nMethods and Process: Under two different multi-robot path planning scenarios, numerical simulations were conducted on robot state transitions, action selection, and environment interaction processes. Centralized and distributed neural policy gradient algorithms were run respectively, with the following data recorded during training: objective function performance metrics (reward for reaching the target, collision penalties, and time penalties), the norm of the gradient of the objective function with respect to policy parameters, and algorithm runtime, which were automatically generated as the corresponding simulation dataset.\r\nData Collection Time and Location: November 2024, completed on the laboratory computing platform.", "ds_quality": "All numerical indicators in this dataset (such as the performance value of the objective function, the policy gradient norm, the running time, etc.) are stored in the form of double-precision floating-point numbers (float64), directly output by the Python and NumPy numerical computing environments, retaining many significant digits after the decimal point, without any manual truncation or rounding processing. It can truly reflect the numerical changes during the simulation process.", "ds_ref_way": "", "ds_abstract": "Dataset of Distributed Neural Policy Gradient Algorithm for Robot Path Planning: This dataset was established in November 2024 and generated using a Python simulation platform, primarily targeting performance analysis and verification of distributed neural policy gradient algorithms in robot path planning tasks. The dataset contains numerical simulation results of centralized and distributed algorithms across two types of path planning scenarios, used to verify the convergence performance, cooperative decision-making capability, and computational efficiency of the proposed algorithm in complex path planning environments. The dataset is constructed based on a multi-robot path planning environment, generating dynamic evolution data of different algorithms during the training process through numerical simulation of robot state transitions, action selections, and environmental interactions. The objective function performance metric is composed of the reward for reaching the target point, the collision penalty incurred between robots or between robots and obstacles, and the time penalty during the path planning process, used to evaluate the comprehensive performance of the algorithm in terms of task completion quality and safety. Meanwhile, the dataset records the norm variation of the objective function gradient with respect to policy parameters, used to analyze the optimization stability and convergence characteristics of the neural policy gradient algorithm. In addition, the runtime of different algorithms during training and decision-making processes is also recorded, used to compare the differences between centralized and distributed methods in terms of computational complexity and real-time performance. All dataset files are in NPY format, comprising 12 files in total, organized by algorithm type and scenario index, with the naming convention \"CTP_PathStructure_AlgorithmTypeDataType.npy\", where path structure is identified by numerical index representing different planning scenarios (11 and 321), algorithm type is divided into centralized (cen) and distributed (dis), and data type includes objective function value (obj), policy gradient norm (gra), and runtime (runningtime), specifically including CTP_11_cenobj.npy, CTP_11_cengra.npy, CTP_11_cenrunningtime.npy, CTP_11_disobj.npy, CTP_11_disgra.npy, CTP_11_disrunningtime.npy, CTP_321_cenobj.npy, CTP_321_cengra.npy, CTP_321_cenrunningtime.npy, CTP_321_disobj.npy, CTP_321_disgra.npy, and CTP_321_disrunningtime.npy. The dataset contains 3 categories of parameters: objective function value (dimensionless, reflecting the comprehensive score of task rewards and penalties), policy gradient norm (dimensionless, reflecting the magnitude of policy parameter updates and used to measure algorithmic convergence stability), and runtime (in seconds, reflecting the computational cost of the algorithm during training and decision-making), with data classified along two dimensions of algorithm type (centralized/distributed) and path scenario (Scenario 11/Scenario 321). In the course of the research, this dataset is used to support the performance verification and effectiveness evaluation of the distributed neural policy gradient algorithm, validate the effectiveness of the proposed distributed policy update mechanism in multi-robot cooperative path planning, and analyze the performance of the algorithm under different communication and cooperative structures in terms of path optimization, collision avoidance, and training efficiency, thereby meeting the simulation verification requirements for robot swarm path planning and cooperative control in complex dynamic environments.", "ds_time_res": "", "ds_acq_place": "Southeast University", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Data Processing Methods:\r\nThis dataset consists of numerical data generated through reinforcement learning algorithm model simulations. The specific processing methods are as follows:\r\nSimulation Environment Construction: A multi-robot path planning simulation environment was built on the Python platform, modeling the robots' state space, action space, reward function, and obstacle distribution.\r\nAlgorithm Models: Centralized policy gradient algorithms and distributed neural policy gradient algorithms were used to train robot path planning policies. The policy network is represented as a neural network, with policy parameters iteratively updated via the policy gradient method. Building on this, the distributed algorithm introduces a mechanism for local information exchange and cooperative updates among multiple agents. The detailed simulation environment structure can be found in the research group's open-source project at https://github.com/Pengcheng-Dai/DACA.\r\nMathematical Operations and Metric Calculations:\r\n\r\nThe objective function performance metric is calculated as a weighted sum of three components: the reward for reaching the target point, the collision penalty (for collisions between robots or with obstacles), and the time penalty, reflecting the overall performance of each episode;\r\nThe policy gradient norm data is obtained by computing the gradient of the objective function with respect to the policy parameters and calculating the Euclidean norm (L2 norm) of this gradient vector, used to measure the convergence trend of the optimization process;\r\nThe runtime data is obtained by timing the training and decision-making computation process for each iteration (or each episode) of the algorithms.\r\n\r\nData Recording and Processing: NumPy was used to perform numerical calculations and statistical aggregation of the various metrics generated during the simulation, which were then stored as numerical arrays categorized by algorithm type (centralized/distributed) and scenario type. Finally, Matplotlib was used to plot convergence curves, performance comparison curves, and runtime statistical charts.", "ds_ref_instruction": "Data format and reading\r\n\r\nThis dataset is stored as a numerical array (such as.npy format) and can be read directly through Python's NumPy library, for example, using functions such as numpy.load() or numpy.loadtxt(). The read data is a multi-dimensional array organized separately by algorithm type (centralized/distributed) and scene type." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "路径规划" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [ 2024 ], "ds_contributors": [ { "true_name": "王和", "email": "wanghe91@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王和", "email": "wanghe91@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "ds_managers": [ { "true_name": "王和", "email": "wanghe91@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "category": "其他" }