AnthillRL: Multi-agent Reinforcement Learning - Heinsfeld, Anibal Sólon; Meneguzzi, Felipe (Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC); 2015: Anais do IX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações, 2015)
Ants are essentially social insects, and their organizational depen dency reflects directly on their survival. Due to their social nature, ant society provides a rich model for analysing properties of multiagent systems such as col laboration and effectiveness of collective action. Modelling natural behaviour of ants can help understand their actions, as well as studying better forms of co operation and competition for other multiagent models. In this paper, we model an ant society within a stochastic environment in which ant behaviour is gen erated using reinforcement learning to generate paths towards their goal. We test this model using a variable number of agents and learning parameters and report on the results.