Evolutionary learning, reinforcement learning, and fuzzy rules for knowledge acquisition in agent-based systems

The behavior of agents in complex and dynamic environments cannot be programmed a priori, but needs to self-adapt to the specific situations. We present some approaches based on evolutionary reinforcement learning algorithms, which are able to evolve in real-time fuzzy models that control behaviors. We discuss an application where an agent learns how to adapt its behavior to the different behaviors of the other agents it is interacting with, and another application where a group of agents co-evolve cooperative behaviors by using explicit communication to propose the cooperation and to distribute reinforcement to the others.