Bucket brigade performance: I. Long sequences of classifiers
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Bucket brigade performance: II. Default hierarchies
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Back Propagation for the Classifier System
Proceedings of the 3rd International Conference on Genetic Algorithms
The Emergence of Coupled Sequences of Classifiers
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning In RoboCup Keepaway Using Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Towards The Use Of XCS In Interactive Evolutionary Design
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Classifier fitness based on accuracy
Evolutionary Computation
Limits in long path learning with XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Improving performance in size-constrained extended classifier systems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Improving small population performance under noise with viral infection + tropism
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
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.