  Capturing and simulating intelligent adaptive behaviours within spatially
explicit individual-based models remains an ongoing challenge for researchers.
While an ever-increasing abundance of real-world behavioural data are
collected, few approaches exist that can quantify and formalise key individual
behaviours and how they change over space and time. Consequently, commonly used
agent decision-making frameworks, such as event-condition-action rules, are
often required to focus only on a narrow range of behaviours. We argue that
these behavioural frameworks often do not reflect real-world scenarios and fail
to capture how behaviours can develop in response to stimuli. There has been an
increased interest in Machine Learning methods and their potential to simulate
intelligent adaptive behaviours in recent years. One method that is beginning
to gain traction in this area is Reinforcement Learning (RL). This paper
explores how RL can be applied to create emergent agent behaviours using a
simple predator-prey Agent-Based Model (ABM). Running a series of simulations,
we demonstrate that agents trained using the novel Proximal Policy Optimisation
(PPO) algorithm behave in ways that exhibit properties of real-world
intelligent adaptive behaviours, such as hiding, evading and foraging.
