Learning enabled cooperative agent behavior in an evolutionary and competitive environment
The proposed method is implemented in three steps: first, when a variation in environment is perceived, agents take appropriate actions. Second, the behaviors are stimulated and controlled through communication with other agents. Finally, the most frequently stimulated behavior is adopted as a group behavior strategy. In this paper, two different reward models, reward model 1 and reward model 2, are applied. Each reward model is designed to consider the reinforcement or constraint of behaviors. In competitive agent environments, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained by reducing the reward values. The validity of this strategy is verified through simulation.
agent artificial life genetic algorithm artificial neural network evolution