{"title":"基于状态向量估计的强化学习在多移动机器人环境下的合作行为获取","authors":"E. Uchibe, M. Asada, K. Hosoda","doi":"10.1109/ROBOT.1998.677351","DOIUrl":null,"url":null,"abstract":"This paper proposes a method that acquires robots' behaviors based on the estimation of the state vectors. In order to acquire the cooperative behaviors in multi-robot environments, each learning robot estimates the local predictive model between the learner and the other objects separately. Based on the local predictive models, the robots learn the desired behaviors using reinforcement learning. The proposed method is applied to a soccer playing situation, where a rolling ball and other moving robots are well modeled and the learner's behaviors are successfully acquired by the method. Computer simulations and real experiments are shown and a discussion is given.","PeriodicalId":272503,"journal":{"name":"Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)","volume":"9 10","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Cooperative behavior acquisition in multi-mobile robots environment by reinforcement learning based on state vector estimation\",\"authors\":\"E. Uchibe, M. Asada, K. Hosoda\",\"doi\":\"10.1109/ROBOT.1998.677351\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method that acquires robots' behaviors based on the estimation of the state vectors. In order to acquire the cooperative behaviors in multi-robot environments, each learning robot estimates the local predictive model between the learner and the other objects separately. Based on the local predictive models, the robots learn the desired behaviors using reinforcement learning. The proposed method is applied to a soccer playing situation, where a rolling ball and other moving robots are well modeled and the learner's behaviors are successfully acquired by the method. Computer simulations and real experiments are shown and a discussion is given.\",\"PeriodicalId\":272503,\"journal\":{\"name\":\"Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)\",\"volume\":\"9 10\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBOT.1998.677351\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBOT.1998.677351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cooperative behavior acquisition in multi-mobile robots environment by reinforcement learning based on state vector estimation
This paper proposes a method that acquires robots' behaviors based on the estimation of the state vectors. In order to acquire the cooperative behaviors in multi-robot environments, each learning robot estimates the local predictive model between the learner and the other objects separately. Based on the local predictive models, the robots learn the desired behaviors using reinforcement learning. The proposed method is applied to a soccer playing situation, where a rolling ball and other moving robots are well modeled and the learner's behaviors are successfully acquired by the method. Computer simulations and real experiments are shown and a discussion is given.