  Advances in reinforcement learning research have demonstrated the ways in
which different agent-based models can learn how to optimally perform a task
within a given environment. Reinforcement leaning solves unsupervised problems
where agents move through a state-action-reward loop to maximize the overall
reward for the agent, which in turn optimizes the solving of a specific problem
in a given environment. However, these algorithms are designed based on our
understanding of actions that should be taken in a real-world environment to
solve a specific problem. One such problem is the ability to identify,
recommend and execute an action within a system where the users are the
subject, such as in education. In recent years, the use of blended learning
approaches integrating face-to-face learning with online learning in the
education context, has in-creased. Additionally, online platforms used for
education require the automation of certain functions such as the
identification, recommendation or execution of actions that can benefit the
user, in this sense, the student or learner. As promising as these scientific
advances are, there is still a need to conduct research in a variety of
different areas to ensure the successful deployment of these agents within
education systems. Therefore, the aim of this study was to contextualise and
simulate the cumulative reward within an environment for an intervention
recommendation problem in the education context.
