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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Evaluation for Selecting Method Using Reinforcement Learning with Hand-Coded Rules in RoboCup Soccer Agents
verfasst von : Hisayuki Sasaoka, Kenji Araki
Erschienen in: Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005)
Verlag: Springer Berlin Heidelberg
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Machine-learning algorithms Reinforcement Learning is one of powerful machine learning algorithms. We have known that this algorithm is the effect of robotic learning and that a lot of researchers have proposed the basic idea using this algorithm. A system in which the algorithm has been built is needs a lot of trials and errors. Moreover it requires a huge amount of calculation in order to achieve some effectiveness. This paper has proposed a method using reinforcement learning with hand-coded rules for a soccer agent in Ro-boCup Soccer Simulation League. We have analyzed a lot of scenes that agents have made scores in simulation soccer games and have extracted some rules. We have called them “hand coded rales”. Moreover we have applied our method for an offensive soccer agent in RoboCup Soccer Simulation League and have done some experiments. From the results, we have confirmed that our team has improved its capability of getting scores.