{"title":"深度学习方法在序列博弈中的应用","authors":"V. Markova, V. Shopov","doi":"10.1109/INFOTECH.2018.8510746","DOIUrl":null,"url":null,"abstract":"This article emphases on the application of deep learning methods in sequential games. The main hypothesis is that sequence to sequence learning approach is applicable and demonstrates better performance than classic reinforcement learning. So autonomous agents trough sequence to sequence approach are able to discover good solutions to the problem at hand by learning in dynamic environment.","PeriodicalId":142221,"journal":{"name":"2018 International Conference on Information Technologies (InfoTech)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Deep Learning Approach in Sequential Games\",\"authors\":\"V. Markova, V. Shopov\",\"doi\":\"10.1109/INFOTECH.2018.8510746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article emphases on the application of deep learning methods in sequential games. The main hypothesis is that sequence to sequence learning approach is applicable and demonstrates better performance than classic reinforcement learning. So autonomous agents trough sequence to sequence approach are able to discover good solutions to the problem at hand by learning in dynamic environment.\",\"PeriodicalId\":142221,\"journal\":{\"name\":\"2018 International Conference on Information Technologies (InfoTech)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Information Technologies (InfoTech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOTECH.2018.8510746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Information Technologies (InfoTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOTECH.2018.8510746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Deep Learning Approach in Sequential Games
This article emphases on the application of deep learning methods in sequential games. The main hypothesis is that sequence to sequence learning approach is applicable and demonstrates better performance than classic reinforcement learning. So autonomous agents trough sequence to sequence approach are able to discover good solutions to the problem at hand by learning in dynamic environment.