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: A Reinforcement Learning Approach to Simulating Offender Behaviours in Criminological Agent-Based Models The data directory is as follows: Model/ Data_Analysis__Notebook.ipynb MC1_Data MC2_Data MC3_Data The Data_Analysis__Notebook.ipynb is the jupyter notebook used to produce the analysis within the article. This notebook requires python 3.* with packages such as matplotlib, seaborn, numpy, pandas, plotly, scipy to run. 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. Each column of the data is described as follows: 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.