Reinforcement learning and co-operation in a simulated multi-agent system
Kostas Kostiadis, Huosheng Hu · 2003
The complexity of most multi-agent systems prohibits a hand-coded approach to decision-making. In addition to that a complex, dynamic, adversarial environment like the one of a football game makes decision-making and cooperation even more difficult. This paper addresses these problems by using machine learning techniques and agent technology. By gathering useful experience from earlier stages, an agent can significantly improve performance. The method used requires no previous knowledge regarding the environment. Since cooperation in adversarial domains is a very challenging task, the proposed learning algorithm assigns each agent a role to play to achieve a certain goal. By distributing the responsibilities among the agents and linking their goals, an efficient way of cooperation emerges.