Does a Q-Learning NetLogo Extension Simplify the Development of Agent-based Simulations? - Bazzanella, Eloísa; Santos, Fernando (Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC); 2021: Anais do XV Workshop-Escola de Sistemas de Agentes, seus Ambientes e AplicaçõesDO - 10.5753/wesaac.2021.33403, 2021)
Agent-based modeling and simulation is a simulation paradigm that allows focusing on individuals, their interactions, and the complex behavior that emerges from them. Agent-based simulations are typically developed in simulation platforms that provide features related to agents. One such platform is NetLogo, to which a reinforcement learning extension was made available recently. The extension provides commands for using the Q-Learning algorithm, but no evaluation on whether it simplifies the development of simulations is available. This paper presents a quantitative evaluation on using the extension in two simulations: the classic cliff walking problem; and a real-world, adaptive traffic signal control (ATSC) simulation. Results show that the size of simulations source code developed using the extension is smaller than those developed without using it, giving evidence that the extension simplifies the development of simulations