{"title":"雅达利《太空入侵者》的深度q -网络体验优化(DQN-EO)及其性能评估","authors":"Elis Kulla","doi":"10.4018/ijdst.296249","DOIUrl":null,"url":null,"abstract":"During recent years, the deep Q-Learning is used to solve different complex problems in different fields. However, Deep Q-Learning does not have a unified method for solving certain problems because different problems require specific settings and parameters. This paper proposes a Deep Q-Network with Experience Optimization for Atari’s “Space Invaders” environment called DQN-EO. Training and testing results are presented. The performance evaluation results show that while using the proposed algorithm the agent is better at avoiding enemy bullets by 37.7% (longer lifetime) and destroying enemy ships by 14.5% (higher score).","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation\",\"authors\":\"Elis Kulla\",\"doi\":\"10.4018/ijdst.296249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During recent years, the deep Q-Learning is used to solve different complex problems in different fields. However, Deep Q-Learning does not have a unified method for solving certain problems because different problems require specific settings and parameters. This paper proposes a Deep Q-Network with Experience Optimization for Atari’s “Space Invaders” environment called DQN-EO. Training and testing results are presented. The performance evaluation results show that while using the proposed algorithm the agent is better at avoiding enemy bullets by 37.7% (longer lifetime) and destroying enemy ships by 14.5% (higher score).\",\"PeriodicalId\":118536,\"journal\":{\"name\":\"Int. J. Distributed Syst. Technol.\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Distributed Syst. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdst.296249\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Distributed Syst. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijdst.296249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation
During recent years, the deep Q-Learning is used to solve different complex problems in different fields. However, Deep Q-Learning does not have a unified method for solving certain problems because different problems require specific settings and parameters. This paper proposes a Deep Q-Network with Experience Optimization for Atari’s “Space Invaders” environment called DQN-EO. Training and testing results are presented. The performance evaluation results show that while using the proposed algorithm the agent is better at avoiding enemy bullets by 37.7% (longer lifetime) and destroying enemy ships by 14.5% (higher score).