Multi-agent systems with reinforcement hierarchical neuro-fuzzy models

This paper introduces a new multi-agent model for intelligent agents, called reinforcement learning hierarchical neuro-fuzzy multi-agent system. This class of model uses a hierarchical partitioning of the input space with a reinforcement learning algorithm to overcome limitations of previous RL methods. The main contribution of the new system is to provide a flexible and generic model for multi-agent environments. The proposed generic model can be used in several applications, including competitive and cooperative problems, with the autonomous capacity to create fuzzy rules and expand their own rule structures, extracting knowledge from the direct interaction between the agents and the environment, without any use of supervised algorithms. The proposed model was tested in three different case studies, with promising results. The tests demonstrated that the developed system attained good capacity of convergence and coordination among the autonomous intelligent agents.