Agent-Based Reinforcement Learning Model of Burglary (ARLMB) datasets for article: Learning the Rational Choice Perspective: A Reinforcement Learning Approach to Simulating Offender Behaviours in Criminological Agent-Based Models

This data deposit contains synthetically generated crime data from the Agent-Based Reinforcement Learning Model of Burglary developed for the research article: Learning the Rational Choice Perspective:<em> A Reinforcement Learning Approach to Simulating Offender Behaviours in Criminological Agent-Based Models</em> <br> The data directory is as follows: <br> Model/ Data_Analysis__Notebook.ipynb MC1_Data MC2_Data MC3_Data <br> The <strong>Data_Analysis__Notebook.ipynb</strong> is the jupyter notebook used to produce the analysis within the article. This notebook requires <strong>python 3.* </strong>with packages such as <strong>matplotlib, seaborn, numpy, pandas, plotly, scipy</strong> to run. <br> The MC1, MC2 and MC3 folders contain the .txt files containing the data outputs used for analysis in the article. Where MC1 = Experiment Condition 1 in the article. <br> Each column of the data is described as follows: <br> <br> AgentID: A unique agent identifier. Action: The current action an agent has chosen can be one of [OFFEND, DON'T OFFEND, MOVE]. Area: The locality in which the above action has taken place. Target_Attractiveness: The target attractiveness value of the property that has been victimised. Target_Reward: The reward at the property that has been victimised. Target_Risk: The risk surrounding the property that has been victimised. Target_Effort: The effort of the property victimised by the specific offender agent. Total_Cumulative_Reward: The total sum of Target_Attractiveness acquired by the offender agent. xAxisPos: The x-axis position of the cell the offender agent is currently in. zAxisPos: The y-axis position of the cell the offender agent is currently in. Zone_Travelled_To: The locality the offender agent is currently travelling towards. Episode: The current episode. Distance_To_Home: The normalised Euclidean distance to the offender agent's home node from the current victimised target. Distance_To_Next_Node: The normalised Euclidean distance to the next routine activity node from the current victimised target. Timestep: The current discrete time point. Target_Cumulative_Reward: The total amount of Target_Attractiveness the offender agent wants to achieve. <br> <br> <br> <br> <br> <br> <br> <br> <br> <br>

本数据集存档包含为研究论文《Learning the Rational Choice Perspective: A Reinforcement Learning Approach to Simulating Offender Behaviours in Criminological Agent-Based Models》开发的基于智能体的盗窃强化学习模型（Agent-Based Reinforcement Learning Model of Burglary）生成的人工合成犯罪数据。
数据集的目录结构如下：
Model/、Data_Analysis__Notebook.ipynb、MC1_Data、MC2_Data、MC3_Data

其中**Data_Analysis__Notebook.ipynb**为用于生成论文中分析结果的Jupyter Notebook，该笔记本需基于Python 3.*版本运行，所需依赖包包括matplotlib、seaborn、numpy、pandas、plotly、scipy等。

MC1、MC2、MC3三个文件夹内包含用于论文分析的.txt格式数据输出文件，其中MC1对应论文中的实验条件1（Experiment Condition 1）。

各数据列的含义如下：

AgentID：智能体唯一标识符
Action：智能体当前选择的动作，可选值为[OFFEND（实施盗窃）、DON'T OFFEND（不实施盗窃）、MOVE（移动）]
Area：上述动作发生的辖区
Target_Attractiveness：受害房产的目标吸引力值
Target_Reward：受害房产可获得的作案收益
Target_Risk：受害房产周边的作案风险
Target_Effort：特定作案智能体针对该房产实施作案所需付出的成本
Total_Cumulative_Reward：作案智能体累计获得的目标吸引力总收益
xAxisPos：作案智能体当前所在单元格的x轴坐标
zAxisPos：作案智能体当前所在单元格的y轴坐标（原文命名为z轴位置）
Zone_Travelled_To：作案智能体当前正前往的辖区
Episode：当前实验轮次
Distance_To_Home：当前受害房产与作案智能体家节点之间的归一化欧氏距离
Distance_To_Next_Node：当前受害房产与下一个日常活动节点之间的归一化欧氏距离
Timestep：当前离散时间步
Target_Cumulative_Reward：作案智能体期望达成的目标吸引力总收益