RL is a paradigm for learning by trial-and-error inspired by the way humans learn new tasks. In a typical RL setup, an agent is tasked with observing its current state in a digital environment and taking actions that maximise accrual of a long-term reward it has been set. The agent receives feedback from the environment as a result of each action such that it knows whet…