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>