Reinforcement Learning in Strategy Selection for a Coordinated Multirobot System

This correspondence presents a multistrategy decision making system for robot soccer games. Through reinforcement processes, the coordination between robots is learned in the course of game. Meanwhile, a better action can be granted after an iterative learning process. The experimental scenario is a five-versus-five soccer game, where the proposed system dynamically assigns each player to a position in a primitive role, such as attacker, goalkeeper, etc. The responsibility of each player varies along with the change of the role in state transitions. Therefore, the system uses several strategies, such as offensive strategy, defensive strategy, and so on, for a variety of scenarios. Thus, the decision-making mechanism can choose a better strategy according to the circumstances encountered. In each strategy, a robot should behave in coordination with its teammates and resolve conflicts aggressively. The major task assignment to robots in each strategy is simply to catch good positions. Therefore, the problem of dispatching robots to good positions in a reasonable manner should be effectively handled with. This kind of problem is similar to assignment problems in linear programming research. Utilizing the Hungarian method, each robot can be assigned to its assigned spot with minimal cost. Consequently, robots based on the proposed decision-making system can accomplish each situational task in coordination.