Simulation for behavior learning of multi-agent robot

In our research, the evolutionary algorithm is applied to behavior learning of an individual agent in multi agent robots. Each robot, which is an agent, is given two behavior duties, collision avoidance from other agents and target (food point) reaching for recovering self-energy. Addressing the problem of two conflicting behaviors, collision avoidance and target reaching motion of multi-agent robots, the learning method to change the self-energy and the behavior gain of each agent is discussed in this paper. Each agent has the same rules and is controlled as a homogeneous distributed system without any central or hierarchical control. Furthermore, we perform a simulation with the additional algorithm of a group evolution in which the parameters of the most excellent agent are copied to a dead agent, that is, an agent that has lost its energy. The simulation confirmed that each agent has the abilities of behavior learning and group evolution.