\section{Challenges in Real-World RL} \cite(Dulac-Arnold, Mankowitz \& Hester, 2019) provide a more detailed description of the challenges of applying RL to real-life tasks. In summary, they outline 9 key challenges: \begin{enumerate} \item Training off-line from the fixed logs of an external behaviour policy, \item Learning on the real system from limited samples, \item High-dimensional continuous state and action spaces, \item Safety constraints that should never or at least rarely be violated, \item Tasks that may be partially observable, alternatively viewed as non-stationary or stochastic, \item Reward functions that are unspecified, multi-objective, or risk-sensitive, \item System operators who desire explainable policies and actions, \item Inference that must happen in real-time at the control frequency of the system, and, \item Large and/or unknown delays in the system actuators, sensors, or rewards. \end{enumerate} Where points 1 and 2 match the need for considering generalisability and efficiency respectively and points 4, 7 \& 8 align with our final aim of interpretability.