reward allotment in an event-driven hybrid learning classifier system for online soccer games
This paper describes our study into the concept of using rewards in a classifier system applied to the acquisition of decision-making algorithms for agents in a soccer game. Our aim is to respond to the changing environment of video gaming that has resulted from the growth of the Internet, and to provide bug-free programs in a short time. We have already proposed a bucket brigade algorithm (a reinforcement learning method for classifiers) and a procedure for choosing what to learn depending on the frequency of events with the aim of facilitating real-time learning while a game is in progress. We have also proposed a hybrid system configuration that combines existing algorithm strategies with a classifier system, and we have reported on the effectiveness of this hybrid system. In this paper, we report on the results of performing reinforcement learning with different reward values assigned to reflect differences in the roles performed by forward, midfielder and defense players, and we describe the results obtained when learning is performed with different combinations of success rewards for various type of play such as dribbling and passing. In 200 matches played against an existing soccer game incorporating an algorithm devised by humans, a better win ratio and better convergence were observed compared with the case where learning was performed with no roles assigned to all of the in-game agents.
event-driven learning classifier systems soccer game real-time learning video-game