Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study
This addresses the problem of capturing complex spatial behaviors in simulations for researchers in agent-based modeling, though it is incremental as it builds on existing RL methods.
The paper tackled the challenge of simulating intelligent adaptive behaviors in agent-based models by applying reinforcement learning, specifically the Proximal Policy Optimization algorithm, to a predator-prey model, resulting in agents exhibiting real-world behaviors like hiding, evading, and foraging.
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.