状态聚合求解马尔可夫决策问题在移动机器人中的应用

Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294) Pub Date : 1998-11-10 DOI:10.1109/TAI.1998.744868

Pierre Laroche, F. Charpillet

{"title":"状态聚合求解马尔可夫决策问题在移动机器人中的应用","authors":"Pierre Laroche, F. Charpillet","doi":"10.1109/TAI.1998.744868","DOIUrl":null,"url":null,"abstract":"In this paper we present two state aggregation methods used to build stochastic plans, modelling our environment with Markov decision processes. Classical methods used to compute stochastic plans are highly intractable for problems necessitating a large number of states, such as our robotics application. The use of aggregation techniques allows to reduce the number of states and our methods give nearly optimal plans in a significantly reduced time.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"75 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"State aggregation for solving Markov decision problems an application to mobile robotics\",\"authors\":\"Pierre Laroche, F. Charpillet\",\"doi\":\"10.1109/TAI.1998.744868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present two state aggregation methods used to build stochastic plans, modelling our environment with Markov decision processes. Classical methods used to compute stochastic plans are highly intractable for problems necessitating a large number of states, such as our robotics application. The use of aggregation techniques allows to reduce the number of states and our methods give nearly optimal plans in a significantly reduced time.\",\"PeriodicalId\":424568,\"journal\":{\"name\":\"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)\",\"volume\":\"75 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1998.744868\",\"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 Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1998.744868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

在本文中，我们提出了两种状态聚合方法用于建立随机计划，用马尔可夫决策过程建模我们的环境。用于计算随机计划的经典方法对于需要大量状态的问题是非常棘手的，例如我们的机器人应用。聚合技术的使用允许减少状态的数量，并且我们的方法在显著缩短的时间内给出几乎最优的计划。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

State aggregation for solving Markov decision problems an application to mobile robotics

In this paper we present two state aggregation methods used to build stochastic plans, modelling our environment with Markov decision processes. Classical methods used to compute stochastic plans are highly intractable for problems necessitating a large number of states, such as our robotics application. The use of aggregation techniques allows to reduce the number of states and our methods give nearly optimal plans in a significantly reduced time.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)

自引率

0.00%

发文量